Generative Adversarial Network: a Brief Introduction. Lili Mou
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1 Generative Adversarial Network: a Brief Introduction Lili Mou doublepower.mou@gmail.com
2 Outline Generative adversarial net Conditional generative adversarial net Deep generative image models using Laplacian pyramid of adversarial networks
3 NIPS'14 Deep generative models are less impactful than deep discriminative models, because... Of the difficulty of approximating many intractable probabilistic computations that arise in maximum likelihood estimation and related strategies Of the difficulty of leveraging the benefits of piecewise linear units in the generative context. (My point of view) Generative problems are much more difficult than discriminative ones.
4 A Game Theory Perspective Two agents: Generative model: Generate new samples that are as similar as the data Discriminative model: Distinguish samples in disguise Each agent takes a step in turn
5 Objective of GAN V(D,G) G(z): A generated sample from distribution z D(x) = Estimated (by D) prob. that x is a real data sample D(x)=1: D regards x as a training sample w.p.1 D(x)=0: D regards x as a generative sample w.p.1 The relationship with traditional minimax problem In a two agent WuZi chess, V evaluates how bad the current position is to me.
6 Objective of GAN V(D,G) G(z): A generated sample from distribution z D(x) = Estimated (by D) prob. that x is a real data sample D(x)=1: D regards x as a training sample w.p.1 D(x)=0: D regards x as a generative sample w.p.1 The relationship with traditional minimax problem In a two agent WuZi chess, V evaluates how bad the current position is to me. Mathematicians seem to be pessimistic creatures who think in terms of losses. Decision theorists in economics and business talk instead in terms of gains (utility).
7 Objective of GAN G(z): A generated sample from distribution z D(x) = Estimated (by D) prob. that x is a real data sample D(x)=1: D regards x as a training sample w.p.1 D(x)=0: D regards x as a generative sample w.p.1 The relationship with traditional minimax problem In a two agent WuZi chess, V evaluates how bad the current position is to me. Adversary's goal: maximize V My Goal: minimize max V V(D,G)
8 Objective of GAN V(D,G) Algorithm max V D
9 Remarks Choose k = 1. Recall CD k minimize log(1 D(G(z))) <==> maximize log D(G(z)) The latter yields a larger gradient especially at beginning steps
10 Discriminative distribution, i.e., how likely a sample is in training set Data distribution Generative distribution Intuitive Explanation
11 Discriminative distribution, i.e., how likely a sample is in training set Data distribution Generative distribution Intuitive Explanation Two point distribution
12 Discriminative distribution, i.e., how likely a sample is in training set Data distribution Generative distribution Intuitive Explanation
13 Discriminative distribution, i.e., how likely a sample is in training set Data distribution Generative distribution Intuitive Explanation
14 Theoretical Analysis
15 Gobal minimum: p_g = p_data The cost is convex in p_g. But in practice, we always train a parametric model G(z;θg)
16
17 Outline Generative adversarial net Conditional generative adversarial net Deep generative image models using Laplacian pyramid of adversarial networks
18 Conditional Generative Adversarial Nets
19 Multi Modal Generation Image convnet, word embeddings pretrained G: D:
20 Multi Modal Generation Image convnet, word embeddings pretrained G: Gaussian noise + image features regression over word embedding D: image + embedding sigmoid
21 Outline Generative adversarial net Conditional generative adversarial net Deep generative image models using Laplacian pyramid of adversarial networks
22 NIPS'15 Laplacian Pyramid Upsample: an i X i image an 2i X 2i image Downsample: an i X i image an i/2 X i/2 image
23 Training
24 Downsample Upsample Training
25 Training Groundtruth Residual Generation
26 Training Each step trained separately
27 Generation
28 Results
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