title

MIT 6.S191: Deep Generative Modeling

description

MIT Introduction to Deep Learning 6.S191: Lecture 4
Deep Generative Modeling
Lecturer: Ava Amini
2023 Edition
For all lectures, slides, and lab materials: http://introtodeeplearning.com
Lecture Outline
0:00 - Introduction
5:48 - Why care about generative models?
7:33 - Latent variable models
9:30 - Autoencoders
15:03 - Variational autoencoders
21:45 - Priors on the latent distribution
28:16 - Reparameterization trick
31:05 - Latent perturbation and disentanglement
36:37 - Debiasing with VAEs
38:55 - Generative adversarial networks
41:25 - Intuitions behind GANs
44:25 - Training GANs
50:07 - GANs: Recent advances
50:55 - Conditioning GANs on a specific label
53:02 - CycleGAN of unpaired translation
56:39 - Summary of VAEs and GANs
57:17 - Diffusion Model sneak peak
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detail

{'title': 'MIT 6.S191: Deep Generative Modeling', 'heatmap': [{'end': 935.288, 'start': 895.228, 'weight': 0.771}, {'end': 1020.317, 'start': 959.464, 'weight': 0.74}, {'end': 1117.729, 'start': 1074.571, 'weight': 0.742}, {'end': 1365.153, 'start': 1222.028, 'weight': 0.86}, {'end': 2373.615, 'start': 2335.828, 'weight': 1}], 'summary': 'Covers deep generative modeling, generative modeling applications, autoencoders, variational autoencoders, regularization in vaes, vaes and gans, unpaired translation with cyclegan, and recent advances in generative ai, showcasing their impact on tasks like facial detection and uncovering biases.', 'chapters': [{'end': 85.63, 'segs': [{'end': 48.202, 'src': 'embed', 'start': 4.781, 'weight': 0, 'content': [{'end': 18.969, 'text': "you I'm really, really excited about this lecture because as Alexander introduced yesterday, right now we're in this tremendous age of generative AI.", 'start': 4.781, 'duration': 14.188}, {'end': 24.033, 'text': "And today we're going to learn the foundations of deep generative modeling,", 'start': 19.63, 'duration': 4.403}, {'end': 36.642, 'text': "where we're going to talk about building systems that can not only look for patterns in data but can actually go a step beyond this to generate brand new data instances based on those learned patterns.", 'start': 24.033, 'duration': 12.609}, {'end': 40.838, 'text': 'This is an incredibly complex and powerful idea.', 'start': 37.816, 'duration': 3.022}, {'end': 48.202, 'text': "And, as I mentioned, it's a particular subset of deep learning that has actually really exploded in the past couple of years,", 'start': 40.998, 'duration': 7.204}], 'summary': 'Exciting lecture on deep generative modeling in the age of generative ai, a subset of deep learning.', 'duration': 43.421, 'max_score': 4.781, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk4781.jpg'}], 'start': 4.781, 'title': 'Deep generative modeling', 'summary': 'Explores the foundations of deep generative modeling, a subset of deep learning that has exploded in the past couple of years, allowing systems to generate brand new data instances based on learned patterns, demonstrated through a visual illusion of three faces.', 'chapters': [{'end': 85.63, 'start': 4.781, 'title': 'Deep generative modeling', 'summary': 'Explores the foundations of deep generative modeling, a subset of deep learning that has exploded in the past couple of years, allowing systems to generate brand new data instances based on learned patterns, demonstrated through a visual illusion of three faces.', 'duration': 80.849, 'highlights': ['The chapter explores the foundations of deep generative modeling, a subset of deep learning that has exploded in the past couple of years, allowing systems to generate brand new data instances based on learned patterns.', 'The lecture emphasizes the power of deep generative modeling by demonstrating a visual illusion of three faces, challenging the audience to identify the real face, revealing that all choices were incorrect.', 'Deep generative modeling is an incredibly complex and powerful idea that has particularly exploded in the past couple of years.']}], 'duration': 80.849, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk4781.jpg', 'highlights': ['Deep generative modeling allows systems to generate brand new data instances based on learned patterns.', 'The lecture emphasizes the power of deep generative modeling through a visual illusion of three faces.', 'Deep generative modeling has particularly exploded in the past couple of years.']}, {'end': 525.48, 'segs': [{'end': 244.181, 'src': 'embed', 'start': 186.813, 'weight': 0, 'content': [{'end': 193.855, 'text': "captures the types of models that we're going to talk about today in the focus on generative modeling,", 'start': 186.813, 'duration': 7.042}, {'end': 202.438, 'text': "which is an example of unsupervised learning and is united by this goal of the problem, where we're given only samples from a training set.", 'start': 193.855, 'duration': 8.583}, {'end': 209.12, 'text': 'And we want to learn a model that represents the distribution of the data that the model is seeing.', 'start': 203.338, 'duration': 5.782}, {'end': 213.732, 'text': 'Generative modeling takes two general forms.', 'start': 210.39, 'duration': 3.342}, {'end': 218.734, 'text': 'First, density estimation, and second, sample generation.', 'start': 214.792, 'duration': 3.942}, {'end': 221.255, 'text': 'In density estimation.', 'start': 219.975, 'duration': 1.28}, {'end': 225.717, 'text': 'the task is given some data examples.', 'start': 221.255, 'duration': 4.462}, {'end': 235.582, 'text': 'our goal is to train a model that learns a underlying probability distribution that describes where the data came from.', 'start': 225.717, 'duration': 9.865}, {'end': 244.181, 'text': 'With sample generation, the idea is similar, but the focus is more on actually generating new instances.', 'start': 237.236, 'duration': 6.945}], 'summary': 'Generative modeling encompasses density estimation and sample generation for learning data distribution.', 'duration': 57.368, 'max_score': 186.813, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk186813.jpg'}, {'end': 291.552, 'src': 'embed', 'start': 267.205, 'weight': 4, 'content': [{'end': 273.007, 'text': 'Now, in both these cases of density estimation and sample generation, the underlying question is the same.', 'start': 267.205, 'duration': 5.802}, {'end': 284.391, 'text': 'Our learning task is to try to build a model that learns this probability distribution that is as close as possible to the true data distribution.', 'start': 273.987, 'duration': 10.404}, {'end': 291.552, 'text': 'Okay so, with this definition and this concept of generative modeling,', 'start': 286.607, 'duration': 4.945}], 'summary': 'Learning model approximates true data distribution for density estimation and sample generation.', 'duration': 24.347, 'max_score': 267.205, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk267205.jpg'}, {'end': 363.205, 'src': 'embed', 'start': 336.876, 'weight': 1, 'content': [{'end': 344.639, 'text': 'And it can be the case that our training data may be very, very biased towards particular features without us even realizing this.', 'start': 336.876, 'duration': 7.763}, {'end': 356.022, 'text': 'Using generative models, we can actually identify the distributions of these underlying features in a completely automatic way, without any labeling,', 'start': 345.919, 'duration': 10.103}, {'end': 363.205, 'text': 'in order to understand what features may be overrepresented in the data, what features may be underrepresented in the data.', 'start': 356.022, 'duration': 7.183}], 'summary': 'Generative models identify biased features in training data automatically.', 'duration': 26.329, 'max_score': 336.876, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk336876.jpg'}, {'end': 437.823, 'src': 'embed', 'start': 409.654, 'weight': 2, 'content': [{'end': 416.458, 'text': 'including edge cases like a deer coming in front of the car or some unexpected, rare events,', 'start': 409.654, 'duration': 6.804}, {'end': 422.261, 'text': 'not just the typical straight freeway driving that it may see the majority of the time.', 'start': 416.458, 'duration': 5.803}, {'end': 435.042, 'text': 'With generative models, we can use this idea of density estimation to be able to identify rare and anomalous events within the training data and,', 'start': 423.396, 'duration': 11.646}, {'end': 437.823, 'text': "as they're occurring, as the model sees them for the first time.", 'start': 435.042, 'duration': 2.781}], 'summary': 'Generative models can identify rare events like deer in front of a car using density estimation.', 'duration': 28.169, 'max_score': 409.654, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk409654.jpg'}], 'start': 86.871, 'title': 'Generative modeling', 'summary': 'Delves into the power and applications of generative modeling, emphasizing the contrast between supervised and unsupervised learning, and discussing the deployment for uncovering biases in facial detection models and identifying rare events in self-driving cars.', 'chapters': [{'end': 291.552, 'start': 86.871, 'title': 'Generative modeling overview', 'summary': 'Discusses the power and applications of generative modeling, highlighting the contrast between supervised and unsupervised learning, emphasizing the goals and techniques of generative modeling, including density estimation and sample generation.', 'duration': 204.681, 'highlights': ['Generative modeling encompasses unsupervised learning, focusing on understanding the hidden structure of data and enabling the generation of new data instances.', 'It involves two general forms: density estimation, aiming to learn the underlying probability distribution of the data, and sample generation, focused on generating new instances similar to the real data distribution.', 'The models trained for generative modeling aim to represent the distribution of the data as closely as possible, providing insights into the foundational representation of the data.', 'Demonstration of the power of generative modeling is shown through the synthesis of fake human faces by deep generative models, illustrating the capabilities of generative modeling techniques.']}, {'end': 525.48, 'start': 291.552, 'title': 'Applications of generative modeling', 'summary': 'Discusses the deployment of generative models for high-impact applications such as uncovering biases in facial detection models and identifying rare events in self-driving cars, using the underlying concept of generative modeling and latent variable models.', 'duration': 233.928, 'highlights': ['Generative models can identify biases in facial detection data, uncovering overrepresented and underrepresented features without labeling.', 'Generative models can be used for outlier detection in self-driving cars, identifying rare and anomalous events within the training data.', "The chapter introduces latent variable models, emphasizing the concept of latent variables through the example of Plato's myth of the cave."]}], 'duration': 438.609, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk86871.jpg', 'highlights': ['Generative modeling encompasses unsupervised learning, focusing on understanding the hidden structure of data and enabling the generation of new data instances.', 'Generative models can identify biases in facial detection data, uncovering overrepresented and underrepresented features without labeling.', 'Generative models can be used for outlier detection in self-driving cars, identifying rare and anomalous events within the training data.', 'It involves two general forms: density estimation, aiming to learn the underlying probability distribution of the data, and sample generation, focused on generating new instances similar to the real data distribution.', 'The models trained for generative modeling aim to represent the distribution of the data as closely as possible, providing insights into the foundational representation of the data.']}, {'end': 1396.242, 'segs': [{'end': 650.19, 'src': 'embed', 'start': 607.266, 'weight': 0, 'content': [{'end': 611.129, 'text': "It's an encoded representation of those underlying features.", 'start': 607.266, 'duration': 3.863}, {'end': 615.753, 'text': "And that's our goal in trying to train this model and predict those features.", 'start': 611.95, 'duration': 3.803}, {'end': 626.702, 'text': "The reason a model like this is called an encoder or an autoencoder is that it's mapping the data, x, into this vector of latent variables, z.", 'start': 617.274, 'duration': 9.428}, {'end': 629.886, 'text': "Now, let's ask ourselves a question.", 'start': 628.106, 'duration': 1.78}, {'end': 630.846, 'text': "Let's pause for a moment.", 'start': 629.926, 'duration': 0.92}, {'end': 650.19, 'text': 'Why may we care about having this latent variable vector z be in a low dimensional space? Anyone have any ideas? All right.', 'start': 631.787, 'duration': 18.403}], 'summary': 'Goal: train model to predict latent variables, z, in low-dimensional space.', 'duration': 42.924, 'max_score': 607.266, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk607266.jpg'}, {'end': 935.288, 'src': 'heatmap', 'start': 873.662, 'weight': 1, 'content': [{'end': 881.184, 'text': 'compressed hidden latent layer to try to bring the network down to learn a compact, efficient representation of the data.', 'start': 873.662, 'duration': 7.522}, {'end': 884.024, 'text': "We don't require any labels.", 'start': 882.144, 'duration': 1.88}, {'end': 885.685, 'text': 'This is completely unsupervised.', 'start': 884.064, 'duration': 1.621}, {'end': 893.847, 'text': "And so in this way, we're able to automatically encode information within the data itself to learn this latent space.", 'start': 886.265, 'duration': 7.582}, {'end': 898.491, 'text': 'auto-encoding information, auto-encoding data.', 'start': 895.228, 'duration': 3.263}, {'end': 902.554, 'text': 'Now, this is a pretty simple model.', 'start': 900.132, 'duration': 2.422}, {'end': 910.66, 'text': 'And it turns out that, in practice, this idea of self-encoding or auto-encoding has a bit of a twist on it,', 'start': 903.374, 'duration': 7.286}, {'end': 917.706, 'text': 'to allow us to actually generate new examples that are not only reconstructions of the input data itself.', 'start': 910.66, 'duration': 7.046}, {'end': 922.569, 'text': 'And this leads us to the concept of variational auto-encoders or VAEs.', 'start': 918.606, 'duration': 3.963}, {'end': 928.161, 'text': 'With the traditional autoencoder that we just saw.', 'start': 924.778, 'duration': 3.383}, {'end': 935.288, 'text': 'if we pay closer attention to the latent layer which is shown in that orange salmon color,', 'start': 928.161, 'duration': 7.127}], 'summary': 'Unsupervised learning uses autoencoders to create compact data representations and generate new examples, such as vaes.', 'duration': 54.499, 'max_score': 873.662, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk873662.jpg'}, {'end': 1033.619, 'src': 'heatmap', 'start': 959.464, 'weight': 3, 'content': [{'end': 970.563, 'text': 'In contrast, Variational autoencoders, VAEs, introduce a element of randomness, a probabilistic twist on this idea of autoencoding.', 'start': 959.464, 'duration': 11.099}, {'end': 980.768, 'text': 'What this will allow us to do is to actually generate new images or new data instances that are similar to the input data,', 'start': 971.383, 'duration': 9.385}, {'end': 983.71, 'text': 'but not forced to be strict reconstructions.', 'start': 980.768, 'duration': 2.942}, {'end': 993.578, 'text': "In practice, with the variational autoencoder, we've replaced that single deterministic layer with a random sampling operation.", 'start': 985.071, 'duration': 8.507}, {'end': 999.883, 'text': 'Now, instead of learning just the latent variables directly themselves,', 'start': 995.559, 'duration': 4.324}, {'end': 1008.81, 'text': 'for each latent variable we define a mean and a standard deviation that captures a probability distribution over that latent variable.', 'start': 999.883, 'duration': 8.927}, {'end': 1020.317, 'text': "What we've done is we've gone from a single vector of latent variable z to a vector of means mu and a vector of standard deviations, sigma,", 'start': 1010.575, 'duration': 9.742}, {'end': 1024.718, 'text': 'that parametrize the probability distributions around those latent variables.', 'start': 1020.317, 'duration': 4.401}, {'end': 1033.619, 'text': 'What this will allow us to do is now sample, using this element of randomness, this element of probability,', 'start': 1026.678, 'duration': 6.941}], 'summary': 'Variational autoencoders introduce randomness to generate similar but not strict reconstructions, using means and standard deviations for probability distributions.', 'duration': 23.044, 'max_score': 959.464, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk959464.jpg'}, {'end': 1117.729, 'src': 'heatmap', 'start': 1074.571, 'weight': 0.742, 'content': [{'end': 1086.623, 'text': 'The encoder is computing a probability distribution of the latent variable z given input data x, while the decoder is doing the inverse,', 'start': 1074.571, 'duration': 12.052}, {'end': 1094.05, 'text': 'trying to learn a probability distribution back in the input data space given the latent variables z.', 'start': 1086.623, 'duration': 7.427}, {'end': 1105.459, 'text': 'And we define separate sets of weights, phi and theta, to define the network weights for the encoder and decoder components of the VAE.', 'start': 1095.351, 'duration': 10.108}, {'end': 1107.841, 'text': 'All right.', 'start': 1107.541, 'duration': 0.3}, {'end': 1117.729, 'text': 'So when we get now to how we actually optimize and learn the network weights in the VAE, the first step is to define a loss function right?', 'start': 1108.461, 'duration': 9.268}], 'summary': 'Encoder and decoder in vae use separate weights to compute and learn probability distributions of latent variables and input data.', 'duration': 43.158, 'max_score': 1074.571, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk1074571.jpg'}, {'end': 1365.153, 'src': 'heatmap', 'start': 1222.028, 'weight': 0.86, 'content': [{'end': 1226.093, 'text': 'The second term, the regularization term, is now where things get a bit more interesting.', 'start': 1222.028, 'duration': 4.065}, {'end': 1229.557, 'text': "So let's go on into this in a little bit more detail.", 'start': 1226.233, 'duration': 3.324}, {'end': 1243.618, 'text': "Because we have this probability distribution and we're trying to compute this encoding and then decode back up As part of regularizing.", 'start': 1230.979, 'duration': 12.639}, {'end': 1249.405, 'text': 'we want to take that inference over the latent distribution and constrain it.', 'start': 1243.618, 'duration': 5.787}, {'end': 1250.966, 'text': 'to behave nicely, if you will.', 'start': 1249.405, 'duration': 1.561}, {'end': 1256.472, 'text': 'The way we do that is we place what we call a prior on the latent distribution.', 'start': 1251.807, 'duration': 4.665}, {'end': 1263.818, 'text': 'And what this is is some initial hypothesis or guess about what that latent variable space may look like.', 'start': 1257.29, 'duration': 6.528}, {'end': 1271.888, 'text': 'This helps us and helps the network to enforce a latent space that roughly tries to follow this prior distribution.', 'start': 1264.679, 'duration': 7.209}, {'end': 1281.531, 'text': "And this prior is denoted as p of z, right? That term d, that's effectively the regularization term.", 'start': 1273.249, 'duration': 8.282}, {'end': 1292.04, 'text': "It's capturing a distance between our encoding of the latent variables and our prior hypothesis about what the structure of that latent space should look like.", 'start': 1282.312, 'duration': 9.728}, {'end': 1294.422, 'text': 'So, over the course of training,', 'start': 1292.941, 'duration': 1.481}, {'end': 1304.251, 'text': "we're trying to enforce that each of those latent variables adopts a probability distribution that's similar to that prior.", 'start': 1294.422, 'duration': 9.829}, {'end': 1316.3, 'text': 'A common choice when training VAEs and developing these models is to enforce the latent variables to be roughly standard.', 'start': 1307.017, 'duration': 9.283}, {'end': 1324.083, 'text': 'normal Gaussian distributions, meaning that they are centered around, mean zero and they have a standard deviation of one.', 'start': 1316.3, 'duration': 7.783}, {'end': 1334.489, 'text': 'What this allows us to do is to encourage the encoder to put the latent variables roughly around a centered space,', 'start': 1325.444, 'duration': 9.045}, {'end': 1348.616, 'text': "distributing the encoding smoothly so that we don't get too much divergence away from that smooth space which can occur if the network tries to cheat and try to simply memorize the data.", 'start': 1334.489, 'duration': 14.127}, {'end': 1355.049, 'text': 'By placing the Gaussian standard normal prior on the latent space,', 'start': 1350.407, 'duration': 4.642}, {'end': 1365.153, 'text': 'we can define a concrete mathematical term that captures the divergence between our encoded latent variables and this prior.', 'start': 1355.049, 'duration': 10.104}], 'summary': 'Regularization term enforces latent variables to follow standard normal gaussian distributions during vae training.', 'duration': 143.125, 'max_score': 1222.028, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk1222028.jpg'}, {'end': 1281.531, 'src': 'embed', 'start': 1257.29, 'weight': 4, 'content': [{'end': 1263.818, 'text': 'And what this is is some initial hypothesis or guess about what that latent variable space may look like.', 'start': 1257.29, 'duration': 6.528}, {'end': 1271.888, 'text': 'This helps us and helps the network to enforce a latent space that roughly tries to follow this prior distribution.', 'start': 1264.679, 'duration': 7.209}, {'end': 1281.531, 'text': "And this prior is denoted as p of z, right? That term d, that's effectively the regularization term.", 'start': 1273.249, 'duration': 8.282}], 'summary': 'Enforcing a latent space following a prior distribution to aid network', 'duration': 24.241, 'max_score': 1257.29, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk1257290.jpg'}, {'end': 1396.242, 'src': 'embed', 'start': 1366.214, 'weight': 5, 'content': [{'end': 1368.955, 'text': 'And this is called the KL divergence.', 'start': 1366.214, 'duration': 2.741}, {'end': 1377.739, 'text': "When our prior is a standard normal, the KL divergence takes the form of the equation that I'm showing up on the screen.", 'start': 1370.015, 'duration': 7.724}, {'end': 1396.242, 'text': 'But what I want you to really come away with is that the concept of trying to smooth things out and to capture this divergence and this difference between the prior and the latent encoding is all this KL term is trying to capture.', 'start': 1378.851, 'duration': 17.391}], 'summary': 'Kl divergence measures difference between prior and latent encoding.', 'duration': 30.028, 'max_score': 1366.214, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk1366214.jpg'}], 'start': 526.541, 'title': 'Autoencoders and variational autoencoders', 'summary': 'Discusses autoencoders, their goal of learning hidden features, encoding and decoding process, and impact of latent space dimensionality. it also explains variational autoencoders, introducing randomness and transformation to generate new data instances, and the regularization term to enforce a latent space following a prior distribution.', 'chapters': [{'end': 951.72, 'start': 526.541, 'title': 'Understanding autoencoders', 'summary': 'Discusses the concept of autoencoders, focusing on their goal of learning hidden features, the process of encoding and decoding data, and the impact of latent space dimensionality on reconstructions, demonstrating the unsupervised learning approach and the transition to variational auto-encoders (vaes).', 'duration': 425.179, 'highlights': ['Autoencoders aim to learn hidden features and latent variables from observed data, using a compact, efficient representation in a low-dimensional latent space, enabling unsupervised learning and automatic encoding of information within the data itself.', 'The process of training an autoencoder involves encoding the input data, mapping it to a low-dimensional latent space, and then decoding it back to the original data space for reconstruction, without requiring explicit labels and demonstrating unsupervised learning.', 'Variational auto-encoders (VAEs) introduce a twist by incorporating a probabilistic approach, enabling the generation of new examples and adding stochasticity to the latent layer, transitioning from a deterministic layer to a probabilistic one for enhanced model capabilities.']}, {'end': 1396.242, 'start': 951.72, 'title': 'Understanding variational autoencoders', 'summary': 'Explains the concept of variational autoencoders (vaes) and how they introduce randomness to generate new data instances. it details the transformation from a single vector of latent variable z to a vector of means mu and a vector of standard deviations sigma, which parametrize the probability distributions around those latent variables, and the introduction of a regularization term to enforce a latent space that roughly follows a prior distribution, often a standard normal gaussian distribution.', 'duration': 444.522, 'highlights': ['The transformation from a single vector of latent variable z to a vector of means mu and a vector of standard deviations sigma, which parametrize the probability distributions around those latent variables.', 'Introduction of a regularization term to enforce a latent space that roughly follows a prior distribution, often a standard normal Gaussian distribution.', 'Explanation of the KL divergence and its role in capturing the divergence and difference between the prior and the latent encoding, aiming to smooth out the distribution of the latent variables.']}], 'duration': 869.701, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk526541.jpg', 'highlights': ['Autoencoders aim to learn hidden features and latent variables from observed data, using a compact, efficient representation in a low-dimensional latent space, enabling unsupervised learning and automatic encoding of information within the data itself.', 'Variational auto-encoders (VAEs) introduce a twist by incorporating a probabilistic approach, enabling the generation of new examples and adding stochasticity to the latent layer, transitioning from a deterministic layer to a probabilistic one for enhanced model capabilities.', 'The process of training an autoencoder involves encoding the input data, mapping it to a low-dimensional latent space, and then decoding it back to the original data space for reconstruction, without requiring explicit labels and demonstrating unsupervised learning.', 'The transformation from a single vector of latent variable z to a vector of means mu and a vector of standard deviations sigma, which parametrize the probability distributions around those latent variables.', 'Introduction of a regularization term to enforce a latent space that roughly follows a prior distribution, often a standard normal Gaussian distribution.', 'Explanation of the KL divergence and its role in capturing the divergence and difference between the prior and the latent encoding, aiming to smooth out the distribution of the latent variables.']}, {'end': 2267.555, 'segs': [{'end': 1546.324, 'src': 'embed', 'start': 1519.232, 'weight': 1, 'content': [{'end': 1527.696, 'text': 'With regularization, we are able to achieve this by trying to minimize that regularization term.', 'start': 1519.232, 'duration': 8.464}, {'end': 1535.179, 'text': "It's not sufficient to just employ the reconstruction loss alone to achieve this continuity and this completeness.", 'start': 1527.776, 'duration': 7.403}, {'end': 1546.324, 'text': 'Because of the fact that without regularization, just encoding and reconstructing does not guarantee the properties of continuity and completeness.', 'start': 1536.68, 'duration': 9.644}], 'summary': 'Regularization minimizes term to achieve continuity and completeness.', 'duration': 27.092, 'max_score': 1519.232, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk1519232.jpg'}, {'end': 1715.095, 'src': 'embed', 'start': 1688.982, 'weight': 0, 'content': [{'end': 1695.869, 'text': 'deterministic layers to be able to successfully apply gradient descent and the backpropagation algorithm.', 'start': 1688.982, 'duration': 6.887}, {'end': 1707.973, 'text': 'The breakthrough idea that enabled VAEs to be trained completely end-to-end was this idea of re-parameterization within that sampling layer.', 'start': 1697.93, 'duration': 10.043}, {'end': 1712.474, 'text': "And I'll give you the key idea about how this operation works.", 'start': 1709.113, 'duration': 3.361}, {'end': 1715.095, 'text': "It's actually really quite clever.", 'start': 1712.534, 'duration': 2.561}], 'summary': 'Vaes trained end-to-end with re-parameterization for gradient descent and backpropagation success.', 'duration': 26.113, 'max_score': 1688.982, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk1688982.jpg'}, {'end': 1965.269, 'src': 'embed', 'start': 1930.297, 'weight': 2, 'content': [{'end': 1934.54, 'text': 'In this example, the face, as you hopefully can appreciate, is shifting,', 'start': 1930.297, 'duration': 4.243}, {'end': 1941.386, 'text': 'the pose is shifting and all this is driven by is the perturbation of a single latent variable,', 'start': 1934.54, 'duration': 6.846}, {'end': 1946.93, 'text': 'tuning the value of that latent variable and seeing how that affects the decoded reconstruction.', 'start': 1941.386, 'duration': 5.544}, {'end': 1955.957, 'text': 'the network is actually able to learn these different encoded features, these different latent variables, such that,', 'start': 1948.288, 'duration': 7.669}, {'end': 1965.269, 'text': 'by perturbing the values of them individually, we can interpret and make sense of what those latent variables mean and what they represent.', 'start': 1955.957, 'duration': 9.312}], 'summary': 'Network learns to interpret and make sense of latent variables by perturbing them individually.', 'duration': 34.972, 'max_score': 1930.297, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk1930297.jpg'}, {'end': 2140.338, 'src': 'embed', 'start': 2056.388, 'weight': 3, 'content': [{'end': 2062.672, 'text': 'All this term is showing is those two components of the loss, the reconstruction term, the regularization term.', 'start': 2056.388, 'duration': 6.284}, {'end': 2064.413, 'text': "That's what I want you to focus on.", 'start': 2063.152, 'duration': 1.261}, {'end': 2073.531, 'text': 'The idea of latent space disentanglement really arose with this concept of beta VAEs.', 'start': 2065.828, 'duration': 7.703}, {'end': 2078.351, 'text': 'What beta VAEs do is they introduce this parameter, beta.', 'start': 2074.431, 'duration': 3.92}, {'end': 2081.533, 'text': "And what it is, it's a weighting constant.", 'start': 2079.192, 'duration': 2.341}, {'end': 2089.556, 'text': 'The weighting constant controls how powerful that regularization term is in the overall loss of the VAE.', 'start': 2082.132, 'duration': 7.424}, {'end': 2098.658, 'text': 'And it turns out that by increasing the value of beta, you can try to encourage greater disentanglement,', 'start': 2090.666, 'duration': 7.992}, {'end': 2104.406, 'text': 'more efficient encoding to enforce these latent variables to be uncorrelated with each other.', 'start': 2098.658, 'duration': 5.748}, {'end': 2112.768, 'text': "Now, if you're interested in mathematically, why beta VAEs enforce this disentanglement?", 'start': 2105.706, 'duration': 7.062}, {'end': 2117.489, 'text': 'there are many papers in the literature and proofs and discussions as to why this occurs.', 'start': 2112.768, 'duration': 4.721}, {'end': 2119.989, 'text': 'And we can point you in those directions.', 'start': 2117.929, 'duration': 2.06}, {'end': 2124.59, 'text': 'But to get a sense of what this actually affects downstream.', 'start': 2120.649, 'duration': 3.941}, {'end': 2134.895, 'text': 'when we look at face reconstruction as a task of interest with the standard VAE no beta term, or rather a beta of 1,', 'start': 2124.59, 'duration': 10.305}, {'end': 2140.338, 'text': 'you can hopefully appreciate that the features of the rotation of the head,', 'start': 2134.895, 'duration': 5.443}], 'summary': 'Beta vaes use beta parameter to control regularization term, promoting disentanglement for more efficient encoding.', 'duration': 83.95, 'max_score': 2056.388, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk2056388.jpg'}, {'end': 2219.505, 'src': 'embed', 'start': 2191.681, 'weight': 6, 'content': [{'end': 2197.582, 'text': "the core architecture of VAEs that we're going to cover in today's lecture and in this class in general.", 'start': 2191.681, 'duration': 5.901}, {'end': 2202.383, 'text': 'To close this section, and as a final note,', 'start': 2198.843, 'duration': 3.54}, {'end': 2208.865, 'text': 'I want to remind you back to the motivating example that I introduced at the beginning of this lecture facial detection.', 'start': 2202.383, 'duration': 6.482}, {'end': 2210.999, 'text': 'where now, hopefully,', 'start': 2209.878, 'duration': 1.121}, {'end': 2219.505, 'text': "you've understood this concept of latent variable learning and encoding and how this may be useful for a task like facial detection,", 'start': 2210.999, 'duration': 8.506}], 'summary': 'The lecture covers the core architecture of vaes and their application to facial detection.', 'duration': 27.824, 'max_score': 2191.681, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk2191681.jpg'}], 'start': 1397.803, 'title': 'Regularization and latent variable learning in vaes', 'summary': 'Delves into the intuition behind regularization in variational autoencoders (vaes), including the re-parameterization trick and achieving end-to-end training. it also explores latent variable learning in vaes, emphasizing disentangled and independent latent features, especially through beta vaes, with practical implications in tasks like facial detection and uncovering hidden biases.', 'chapters': [{'end': 1976.958, 'start': 1397.803, 'title': 'Intuition behind regularization in vaes', 'summary': 'Explains the intuition behind regularization in variational autoencoders (vaes) and the re-parameterization trick, ensuring continuity and completeness in the latent space, achieving end-to-end training, and interpreting the captured features.', 'duration': 579.155, 'highlights': ['Re-parameterization trick for end-to-end training', 'Importance of regularization for continuity and completeness', 'Interpreting captured features through perturbation of latent variables']}, {'end': 2267.555, 'start': 1976.958, 'title': 'Variational autoencoders: latent variable learning', 'summary': 'Covers the concept of variational autoencoders (vaes) and introduces the idea of disentangled and independent latent features, particularly through beta vaes, which can enforce greater disentanglement and more efficient encoding, leading to practical implications in tasks like facial detection and uncovering hidden biases.', 'duration': 290.597, 'highlights': ['The concept of disentangled and independent latent features is introduced through beta VAEs, which can enforce greater disentanglement and more efficient encoding, particularly in tasks like facial detection and uncovering hidden biases.', 'The use of a weighting constant, beta, in beta VAEs controls the strength of the regularization term in the VAE loss, allowing for the encouragement of greater disentanglement and uncorrelated latent variables.', 'The reconstruction task of facial detection is used as an example, demonstrating that with beta VAEs and imposing beta values much greater than 1, greater disentanglement can be achieved, leading to a more constant representation of latent variables like head pose and smile.', 'The core operations and architecture of VAEs, particularly beta VAEs, are covered, along with their practical implications in tasks like facial detection and uncovering hidden biases in data and models.']}], 'duration': 869.752, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk1397803.jpg', 'highlights': ['The re-parameterization trick for end-to-end training', 'Importance of regularization for continuity and completeness', 'Interpreting captured features through perturbation of latent variables', 'The concept of disentangled and independent latent features introduced through beta VAEs', 'The use of a weighting constant, beta, in beta VAEs controls the strength of the regularization term', 'Reconstruction task of facial detection used as an example for beta VAEs', 'Core operations and architecture of VAEs, particularly beta VAEs, covered']}, {'end': 3173.19, 'segs': [{'end': 2295.303, 'src': 'embed', 'start': 2267.555, 'weight': 0, 'content': [{'end': 2276.418, 'text': 'but actually solve and mitigate some of those harmful effects of those biases in neural networks for facial detection and other applications.', 'start': 2267.555, 'duration': 8.863}, {'end': 2278.878, 'text': 'All right.', 'start': 2278.038, 'duration': 0.84}, {'end': 2288.481, 'text': "so to summarize quickly the key points of VAEs, we've gone through how they're able to compress data into this compact encoded representation.", 'start': 2278.878, 'duration': 9.603}, {'end': 2295.303, 'text': 'From this representation, we can generate reconstructions of the input in a completely unsupervised fashion.', 'start': 2289.261, 'duration': 6.042}], 'summary': 'Vaes compress data into a compact representation to generate unsupervised reconstructions.', 'duration': 27.748, 'max_score': 2267.555, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk2267555.jpg'}, {'end': 2383.256, 'src': 'heatmap', 'start': 2320.171, 'weight': 7, 'content': [{'end': 2326.633, 'text': 'So VAEs are looking at this idea of latent variable encoding and density estimation as their core problem.', 'start': 2320.171, 'duration': 6.462}, {'end': 2334.607, 'text': "What if now we only focus on the quality of the generated samples, and that's the task that we care more about?", 'start': 2327.697, 'duration': 6.91}, {'end': 2342.658, 'text': "For that, we're going to transition to a new type of generative model called a generative adversarial network, or GAN.", 'start': 2335.828, 'duration': 6.83}, {'end': 2353.761, 'text': 'where? with GANs, our goal is really that we care more about how well we generate new instances that are similar to the existing data,', 'start': 2344.175, 'duration': 9.586}, {'end': 2362.067, 'text': 'meaning that we want to try to sample from a potentially very complex distribution that the model is trying to approximate.', 'start': 2353.761, 'duration': 8.306}, {'end': 2370.653, 'text': "It can be extremely, extremely difficult to learn that distribution directly, because it's complex, it's high dimensional,", 'start': 2363.548, 'duration': 7.105}, {'end': 2373.615, 'text': 'and we want to be able to get around that complexity.', 'start': 2370.653, 'duration': 2.962}, {'end': 2383.256, 'text': 'What GANs do is they say okay, what if we start from something super, super simple, as simple as it can get completely random noise?', 'start': 2374.665, 'duration': 8.591}], 'summary': 'Transitioning from vaes to gans to focus on generating high-quality samples from complex data distribution.', 'duration': 63.085, 'max_score': 2320.171, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk2320171.jpg'}, {'end': 2481.228, 'src': 'embed', 'start': 2397.945, 'weight': 1, 'content': [{'end': 2407.992, 'text': 'where the goal is to train this generator network that learns a transformation from noise to the training data distribution,', 'start': 2397.945, 'duration': 10.047}, {'end': 2413.455, 'text': 'with the goal of making the generated examples as close to the real deal as possible.', 'start': 2407.992, 'duration': 5.463}, {'end': 2427.863, 'text': 'With GANs, the breakthrough idea here was to interface these two neural networks together, one being a generator and one being a discriminator.', 'start': 2415.076, 'duration': 12.787}, {'end': 2434.706, 'text': 'And these two components, the generator and discriminator, are at war, at competition with each other.', 'start': 2428.683, 'duration': 6.023}, {'end': 2446.091, 'text': "Specifically, the goal of the generator network is to look at random noise and try to produce an imitation of the data that's as close to real as possible.", 'start': 2435.746, 'duration': 10.345}, {'end': 2454.292, 'text': 'The discriminator then takes the output of the generator, as well as some real data examples,', 'start': 2447.247, 'duration': 7.045}, {'end': 2460.717, 'text': 'and tries to learn a classification decision distinguishing real from fake.', 'start': 2454.292, 'duration': 6.425}, {'end': 2462.517, 'text': 'And effectively.', 'start': 2461.676, 'duration': 0.841}, {'end': 2467.6, 'text': 'in the GAN, these two components are going back and forth, competing each other,', 'start': 2462.517, 'duration': 5.083}, {'end': 2473.423, 'text': 'trying to force the discriminator to better learn this distinction between real and fake,', 'start': 2467.6, 'duration': 5.823}, {'end': 2481.228, 'text': 'while the generator is trying to fool and outperform the ability of the discriminator to make that classification.', 'start': 2473.423, 'duration': 7.805}], 'summary': 'Train a generator network to mimic real data using gans for realistic output.', 'duration': 83.283, 'max_score': 2397.945, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk2397945.jpg'}, {'end': 2900.711, 'src': 'embed', 'start': 2873.13, 'weight': 3, 'content': [{'end': 2882.496, 'text': 'where the generator is trying to synthesize these synthetic examples that ideally fool the best discriminator possible.', 'start': 2873.13, 'duration': 9.366}, {'end': 2889.681, 'text': 'And in doing so, the goal is to build up a network via this adversarial training,', 'start': 2883.316, 'duration': 6.365}, {'end': 2900.711, 'text': 'this adversarial competition to use the generator to create new data that best mimics the true data distribution and is completely synthetic new instances.', 'start': 2889.681, 'duration': 11.03}], 'summary': 'Generator synthesizes data to fool best discriminator, creating new data instances.', 'duration': 27.581, 'max_score': 2873.13, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk2873130.jpg'}, {'end': 3090.532, 'src': 'embed', 'start': 3037.216, 'weight': 4, 'content': [{'end': 3046.523, 'text': 'And this is the approach that was used to generate those images, of those synthetic faces that I showed at the beginning of this lecture,', 'start': 3037.216, 'duration': 9.307}, {'end': 3052.968, 'text': 'this idea of using a GAN that is refined iteratively to produce higher resolution images.', 'start': 3046.523, 'duration': 6.445}, {'end': 3066.534, 'text': 'Another way we can extend this concept is to extend the GAN architecture to consider particular tasks and impose further structure on the network itself.', 'start': 3055.211, 'duration': 11.323}, {'end': 3076.657, 'text': 'One particular idea is to say OK, what if we have a particular label or some factor that we want to condition the generation on?', 'start': 3067.714, 'duration': 8.943}, {'end': 3082.349, 'text': "We call this C and it's supplied to both the generator and the discriminator.", 'start': 3077.947, 'duration': 4.402}, {'end': 3090.532, 'text': 'What this will allow us to achieve is paired translation between different types of data.', 'start': 3083.749, 'duration': 6.783}], 'summary': 'Using gan to generate synthetic faces, extending gan architecture for specific tasks and data translation.', 'duration': 53.316, 'max_score': 3037.216, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk3037216.jpg'}], 'start': 2267.555, 'title': 'Vaes and gans: generative models', 'summary': 'Delves into variational autoencoders (vaes), highlighting their data compression, reconstruction generation, and latent space sampling capabilities. it also explores generative adversarial networks (gans), elucidating their function of generating new data through competitive training between a generator and discriminator.', 'chapters': [{'end': 2508.073, 'start': 2267.555, 'title': 'Vaes and gans: generative models', 'summary': 'Explores variational autoencoders (vaes), emphasizing their ability to compress data, generate reconstructions, and sample from the latent space, then transitions to generative adversarial networks (gans) which focus on generating new instances similar to existing data by training a generator and discriminator in competition.', 'duration': 240.518, 'highlights': ['VAEs compress data into a compact encoded representation and generate reconstructions in an unsupervised fashion.', 'GANs aim to generate new instances similar to existing data by training a generator network from random noise to approximate the data distribution, with a discriminator network distinguishing real from fake examples.', 'The GAN framework involves a generator and a discriminator network in competition, with the generator attempting to produce imitations of the data as close to real as possible and the discriminator learning to distinguish real from fake examples.']}, {'end': 3173.19, 'start': 2508.979, 'title': 'Understanding generative adversarial networks', 'summary': 'Explains the working of generative adversarial networks where the generator and discriminator compete to produce and distinguish real and fake data, aiming to synthesize new data that best mimics the true data distribution.', 'duration': 664.211, 'highlights': ['The generator and discriminator compete to produce and distinguish real and fake data.', 'The objective of the GAN is to synthesize new data that best mimics the true data distribution.', 'Iterative growth of GAN is employed to generate high-resolution images.', 'GAN architecture can be extended to enable translation between different types of data based on specific conditions or labels.']}], 'duration': 905.635, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk2267555.jpg', 'highlights': ['VAEs compress data into a compact encoded representation and generate reconstructions in an unsupervised fashion.', 'GANs aim to generate new instances similar to existing data by training a generator network from random noise to approximate the data distribution, with a discriminator network distinguishing real from fake examples.', 'The GAN framework involves a generator and a discriminator network in competition, with the generator attempting to produce imitations of the data as close to real as possible and the discriminator learning to distinguish real from fake examples.', 'The objective of the GAN is to synthesize new data that best mimics the true data distribution.', 'GAN architecture can be extended to enable translation between different types of data based on specific conditions or labels.', 'The generator and discriminator compete to produce and distinguish real and fake data.', 'Iterative growth of GAN is employed to generate high-resolution images.', 'VAEs and GANs have capabilities for data compression, reconstruction generation, and latent space sampling.']}, {'end': 3587.766, 'segs': [{'end': 3231.377, 'src': 'embed', 'start': 3175.39, 'weight': 2, 'content': [{'end': 3187.283, 'text': 'Finally, again extending the same concept of translation between one domain to another, another idea is that of completely unpaired translation.', 'start': 3175.39, 'duration': 11.893}, {'end': 3191.547, 'text': 'And this uses a particular GAN architecture called CycleGAN.', 'start': 3187.924, 'duration': 3.623}, {'end': 3194.657, 'text': "So, in this video that I'm showing here,", 'start': 3192.777, 'duration': 1.88}, {'end': 3205.059, 'text': "the model takes as input a bunch of images in one domain and it doesn't necessarily have to have a corresponding image in another target domain,", 'start': 3194.657, 'duration': 10.402}, {'end': 3214.441, 'text': 'but it is trained to try to generate examples in that target domain that roughly correspond to the source domain,', 'start': 3205.059, 'duration': 9.382}, {'end': 3218.842, 'text': 'transferring the style of the source onto the target and vice versa.', 'start': 3214.441, 'duration': 4.401}, {'end': 3227.053, 'text': 'So this example is showing the translation of images in horse domain to zebra domain.', 'start': 3219.747, 'duration': 7.306}, {'end': 3231.377, 'text': 'The concept here is this cyclic dependency.', 'start': 3228.154, 'duration': 3.223}], 'summary': 'Cyclegan enables unpaired image translation, e.g. horse to zebra, using cyclic dependency.', 'duration': 55.987, 'max_score': 3175.39, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk3175390.jpg'}, {'end': 3283.338, 'src': 'embed', 'start': 3253.961, 'weight': 1, 'content': [{'end': 3264.128, 'text': "With the cycle GAN, you're trying to go from some source distribution, some data manifold x, to a target distribution, another data manifold y.", 'start': 3253.961, 'duration': 10.167}, {'end': 3274.874, 'text': 'And this is really, really not only cool, but also powerful in thinking about how we can translate across these different distributions flexibly.', 'start': 3265.589, 'duration': 9.285}, {'end': 3283.338, 'text': 'And in fact, this allows us to do transformations not only to images, but to speech and audio as well.', 'start': 3276.074, 'duration': 7.264}], 'summary': 'Cycle gan enables flexible translation across different data distributions, including images, speech, and audio.', 'duration': 29.377, 'max_score': 3253.961, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk3253961.jpg'}, {'end': 3336.358, 'src': 'embed', 'start': 3313.074, 'weight': 3, 'content': [{'end': 3324.376, 'text': "but in fact this was exactly how we developed the model to synthesize the audio behind Obama's voice that we saw in yesterday's introductory lecture.", 'start': 3313.074, 'duration': 11.302}, {'end': 3336.358, 'text': "What we did was we trained a CycleGAN to take data in Alexander's voice and transform it into data in the manifold of Obama's voice.", 'start': 3325.356, 'duration': 11.002}], 'summary': "Developed a model to synthesize audio behind obama's voice using cyclegan and alexander's voice data.", 'duration': 23.284, 'max_score': 3313.074, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk3313074.jpg'}, {'end': 3468.049, 'src': 'embed', 'start': 3440.198, 'weight': 0, 'content': [{'end': 3447.542, 'text': 'truly tremendous advances in generative modeling, many of which have not been from those two methods,', 'start': 3440.198, 'duration': 7.344}, {'end': 3453.666, 'text': 'those two foundational methods that we described, but rather a new approach called diffusion modeling.', 'start': 3447.542, 'duration': 6.124}, {'end': 3464.927, 'text': "Diffusion models are the driving tools behind the tremendous advances in generative AI that we've seen in this past year in particular.", 'start': 3454.861, 'duration': 10.066}, {'end': 3468.049, 'text': 'VAEs, GANs.', 'start': 3466.268, 'duration': 1.781}], 'summary': 'Diffusion modeling drives tremendous advances in generative ai, seen in the past year, particularly with vaes and gans.', 'duration': 27.851, 'max_score': 3440.198, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk3440198.jpg'}, {'end': 3579.684, 'src': 'embed', 'start': 3546.952, 'weight': 5, 'content': [{'end': 3553.536, 'text': "but other fields as well in which we're seeing these models really start to make transformative advances,", 'start': 3546.952, 'duration': 6.584}, {'end': 3559.319, 'text': 'because they are indeed at the very cutting edge and very much the new frontier of generative AI today.', 'start': 3553.536, 'duration': 5.783}, {'end': 3561.259, 'text': 'All right.', 'start': 3560.619, 'duration': 0.64}, {'end': 3569.601, 'text': 'so with that tease and hopefully set the stage for lecture seven on Thursday,', 'start': 3561.259, 'duration': 8.342}, {'end': 3579.684, 'text': 'and conclude and remind you all that we have now about an hour for open office hour time for you to work on your software labs.', 'start': 3569.601, 'duration': 10.083}], 'summary': 'Cutting-edge generative ai models making transformative advances across various fields.', 'duration': 32.732, 'max_score': 3546.952, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk3546952.jpg'}], 'start': 3175.39, 'title': 'Unpaired translation and cycle gan', 'summary': "Delves into unpaired translation using cyclegan, showcasing the transformation of images from one domain to another, and explores the flexibility of cycle gan for transforming different data distributions, exemplified by audio synthesis. additionally, it discusses recent advances in generative ai, emphasizing diffusion models' transformative progress and their position at the cutting edge of generative ai.", 'chapters': [{'end': 3231.377, 'start': 3175.39, 'title': 'Unpaired translation using cyclegan', 'summary': 'Discusses the concept of unpaired translation using cyclegan, where a model is trained to generate examples in a target domain that roughly correspond to the source domain, showcasing the translation of images in the horse domain to the zebra domain.', 'duration': 55.987, 'highlights': ['The model uses CycleGAN to translate images in the horse domain to the zebra domain, showcasing unpaired translation.', 'The model is trained to generate examples in the target domain that correspond to the source domain, demonstrating the transfer of style from the source to the target.', 'The concept involves a cyclic dependency, allowing for the translation of images between domains without requiring corresponding images in the target domain.']}, {'end': 3391.224, 'start': 3231.377, 'title': 'Cycle gan for distribution transformation', 'summary': "Introduces the concept of cycle gan, which allows flexible transformation between different data distributions, enabling the translation of audio and speech, exemplified by the synthesis of obama's voice from alexander's using a cyclegan model.", 'duration': 159.847, 'highlights': ['Cycle GAN enables flexible transformation between different data distributions, allowing translation of audio and speech.', "The use of Cycle GAN for synthesizing audio, demonstrated by transforming Alexander's voice into Obama's voice.", 'The application of Cycle GAN to speech and audio, enabling the transformation of sound waves using spectrogram images.']}, {'end': 3587.766, 'start': 3392.324, 'title': 'Advances in generative ai', 'summary': 'Discusses the recent advances in generative modeling, focusing on diffusion models, which have led to tremendous progress in generating completely new objects and instances, challenging the limits and bounds of ai in creating new instances. the lecture also teases the deep dive into diffusion models in lecture 7, highlighting their transformative advances and position at the cutting edge of generative ai today.', 'duration': 195.442, 'highlights': ['Diffusion models have driven tremendous advances in generative AI, surpassing the limitations of VAEs and GANs, by enabling the generation of completely new objects and instances, expanding the design space beyond training data.', 'The lecture teases the deep dive into diffusion models in Lecture 7, highlighting their transformative advances and position at the cutting edge of generative AI today.', 'The discussion raises questions about the limits and bounds of generative models, as well as their comparison to human capabilities and intelligence.', 'The open office hour is available for an hour for students to work on their software labs and ask any questions they may have.']}], 'duration': 412.376, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3G5hWM6jqPk/pics/3G5hWM6jqPk3175390.jpg', 'highlights': ['Diffusion models drive advances in generative AI, surpassing VAEs and GANs, enabling generation of new objects and instances.', 'CycleGAN enables flexible transformation between different data distributions, allowing translation of audio and speech.', 'CycleGAN translates images between domains without requiring corresponding images in the target domain.', "CycleGAN synthesizes audio, demonstrated by transforming Alexander's voice into Obama's voice.", 'CycleGAN is trained to generate examples in the target domain that correspond to the source domain, demonstrating style transfer.', 'Diffusion models are at the cutting edge of generative AI, teased for a deep dive in Lecture 7.']}], 'highlights': ['Deep generative modeling allows systems to generate brand new data instances based on learned patterns.', 'Generative modeling encompasses unsupervised learning, focusing on understanding the hidden structure of data and enabling the generation of new data instances.', 'Autoencoders aim to learn hidden features and latent variables from observed data, using a compact, efficient representation in a low-dimensional latent space, enabling unsupervised learning and automatic encoding of information within the data itself.', 'Variational auto-encoders (VAEs) introduce a twist by incorporating a probabilistic approach, enabling the generation of new examples and adding stochasticity to the latent layer, transitioning from a deterministic layer to a probabilistic one for enhanced model capabilities.', 'The re-parameterization trick for end-to-end training', 'Diffusion models drive advances in generative AI, surpassing VAEs and GANs, enabling generation of new objects and instances.', 'Generative models can identify biases in facial detection data, uncovering overrepresented and underrepresented features without labeling.', 'Generative models can be used for outlier detection in self-driving cars, identifying rare and anomalous events within the training data.', 'GANs aim to generate new instances similar to existing data by training a generator network from random noise to approximate the data distribution, with a discriminator network distinguishing real from fake examples.', 'CycleGAN enables flexible transformation between different data distributions, allowing translation of audio and speech.']}