GANs Deep Learning Summer School
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This document discusses generative adversarial networks (GANs) and provides several summaries: 1. GANs use two neural networks, a generator and discriminator, that compete in a game theoretic framework to generate new data instances that match the training data distribution. 2. Training GANs involves training the generator to generate more realistic samples to fool the discriminator while training the discriminator to better distinguish real and generated samples. 3. Several strategies for training GANs are discussed, including varying the update rates of the generator and discriminator, using cooperative or random update strategies, and applying penalties for noisy samples.
![G
D
Latent Variables
(add noise)
Fake
Training Data
Real
MLP
MLP
Relu
B
A
b b=[1,1,1,1,1,1,1,1,1,1] N=10
B=[0,0,0,0,0,1,1,1,1,1]
P(B=b) = 0.5
H(B)=-SumP(B=b).logP(B=b)
H(B)=-0.5.log(0.5)
H(B)=-0.15
H(A|B=b)=-0.8.log(0.8)
H(A|B=b)=-0.04
B=[0,0,0,0,0,1,1,1,1,1]
A=[0,0,0,0,0,0,1,1,1,1]
P(A|B=b) = 4/5= 0.8 (subsample)](https://image.slidesharecdn.com/ganssummerschool14layout-190223151115/85/GANs-Deep-Learning-Summer-School-55-320.jpg)
GANs Deep Learning Summer School
- 1. Generative Adversarial Networks Rubens Zimbres Data Scientist Machine Learning for IoT at Vecto Mobile
- 2. Generative Models Discriminative Models Game Theory Prisoner’s Dillemma Nash Equilibrium Rational choice Bounded Rationality Non cooperative Game Training GANs Some examples
- 3. + Noise Player 1 (D) Player 2 (G)
- 4. Generative Models • Probabilistic, samples from joint distribution P(x,y) • Generate samples (latent variables) following a distribution • Maximum Likelihood • Range of values belong to a class
- 5. Generative Models • Latent Dirichlet Allocation (NLP) • Mixture Gaussians • Hidden Markov Model • VAE • RBM • GANs
- 8. Variational Autoencoders Encoder Decoder Blurred Backprop Kullback–Leibler divergence KL (surprise)
- 10. Generative Models X1 X2 Buy Recommend Y2 Y1
- 11. Discriminative Models • Observed variables: target, infer outputs, conditional • Function maps from x to Y • Conditional density function • P(Y|x) subsample • Decision bounday • Computationally expensive
- 12. Discriminative Models • Logistic Regression • SVMs • Neural Networks
- 14. Decision Trees
- 16. Generative vs Discriminative • Generative learns joint probability distribution = P(x,y) • Discriminative learns conditional probability distribution = P(y|x)
- 17. GAN Advantages over Generative Models • D & G = Multi Layer Perceptron • Regular backpropagation like VAE (G and D differentiable) • Does not necessarily involve Maximum Likelihood estimation • No need to use Markov Chains • Subject but robust to overfitting
- 18. Game Theory • 2 Player Game – Zero Sum • Minimax solution: Nash Equilibrium • Unique solution: D=1/2 everywhere
- 20. Nash Equilibrium • Sub optimal • Non cooperative • Emulate human behavior: bounded rationality (Herbert Simon) • Simultaneous but not equivalent ** (GANs)
- 21. GANs • Semi Supervised Learning: missing labels • Inverse Reinforcement Learning
- 23. G D 1 Latent Variables (add noise) Fake Training Data Real MLP MLP Encoder Decoder Latent Variables (add noise) MLP MLP Real MNIST ADVERSARIAL (0,0) COOPERATIVE (1,1)
- 24. Question • How can we change GAN’s strategy to create e WIN-WIN situation with payoff equal to (1,1) ?
- 25. G D 1 Latent Variables Add (noise) Fake Training Data Real MLP MLP ADVERSARIAL (0,0) G COOPERATIVE (1,1) Infiltrated Cop MLP Training Data Real
- 26. GAN Tuning • Neural Network architecture • Hyperparameters • Game Theory strategies
- 28. GANs • Train Simultaneously (no freeze) • Train Discriminator one step • Train Generator k steps (no Nash) • Until pG=pdata (global optimum) • Discriminator cannot distinguish both distributions
- 29. GANs • x comes from pdata y=1 (Real) • x comes from pG y=0 (Fake) • Optimize θ rather than pG • G: Relu (vanishing gradient) • Dropout • Supervised Learning in D: • Beginning: high confidence • Usually D is bigger/deeper than G
- 30. Vanishing Gradient Image source: Andrej Karpathy -25% each activation
- 31. Coefficient and Intercept: Regularization
- 32. Dropout
- 33. • Generator: Gradient Descent on V • Discriminator: Gradient Ascent on V • Minibatch • Batch Normalization in G *** • To minimize Cross Entropy • Minimax Game • Regular cross entropy Training GANs
- 34. Training GANs • Discriminator minimize J(D)(θD, θG) controlling θD • Generator minimize J(G)(θD, θG) controlling θG • Cannot control each other (G still learns)
- 35. Training Challenges • Non convergence: Nash Equilibrium, harder to optimize than objective function • Mode collapse: G fails to output diversity (few good samples) • D converges to right distribution • G generates samples in the most probable point • Solution: k steps for D training for 1 step G training
- 37. Tips and Tricks for GANs • Normalize data (-1,1) • Activation function Tanh output of Generative • Sample from gaussian distribution instead of uniform distribution • Develop different mini-batches in Discriminator: • All real • All fake • Use Batch Normalization
- 38. Tips and Tricks for GANs • Use DCGANs or VAE+GAN • Adam in Generator (exp. momentum decay) (Adaptive Moment Estimation) • SGD in Discriminator • If Generator loss high = garbage to Discriminator • Dropouts in Generator (overfitting)
- 42. Adam Optimizer Momentum w/ exponential decay according to derivative of error
- 43. Optimizers: generalization Wilson et al, 2017
- 44. GAN Examples: Vector Arithmetic
- 45. Vector Arithmetic Radford et al, 2015
- 47. DCGAN • Deep Convolutional GAN • Batch Norm not in end of Generator and NOT in Discriminator (x covariate shift - Kaggle) • To increase dimension, Upsampling: convo2D.T with stride > 1 • Downsampling: average pooling + conv2D + stride
- 48. Convolutions Convolution2D Convo2DTranspose 3x3 kernel 4x4 input stride=1 3x3 kernel 2x2 input stride=1
- 50. DCGAN Training
- 51. DCGAN Output
- 52. DCGAN Output
- 53. DCGAN – Face Recognition
- 54. Info GAN • Based in Mutual Information and Entropy (uncertainty) Coin toss
- 55. G D Latent Variables (add noise) Fake Training Data Real MLP MLP Relu B A b b=[1,1,1,1,1,1,1,1,1,1] N=10 B=[0,0,0,0,0,1,1,1,1,1] P(B=b) = 0.5 H(B)=-SumP(B=b).logP(B=b) H(B)=-0.5.log(0.5) H(B)=-0.15 H(A|B=b)=-0.8.log(0.8) H(A|B=b)=-0.04 B=[0,0,0,0,0,1,1,1,1,1] A=[0,0,0,0,0,0,1,1,1,1] P(A|B=b) = 4/5= 0.8 (subsample)
- 56. Info GAN Output
- 57. Info GAN Output
- 58. Info GAN Output
- 60. GAN Examples • Next frame prediction • Image-to-image translation Lotter et al, 2016 Isola et al, 2016
- 61. GAN Examples • Text-to-Image generation Image Inpainting Reed et al, 2016 Pathak et al, 2016
- 62. Cycle GAN: Style Transfer Zhu et al, 2017
- 66. Different Game Strategies • “Rather than” tuning architecture and hyperparameters • Freeze layers (unofficial implementation) • Discriminator : Generator • 1:1 • 5:1 • 1:5 • Cooperation • Random strategy update • Tit-for-Tat strategy
- 68. Gan Structure
- 69. One Pixel Attack to Fool Neural Nets
- 71. Generated Samples 𝒏𝒐𝒊𝒔𝒆 = 𝒙 + 𝟎. 𝟏 ∗ 𝒓𝒏𝒅 𝟎, 𝟏 𝒙 = 𝟎 𝝈 = 𝟏
- 73. 1:1 Update (Vanilla GAN) G D 1 Latent Variables (add noise) Fake Training Data Real MLP MLP Train Simultaneously Accuracy Training Set: .92 Accuracy Test Set: .914 Nash Equilibrium: Sub-optimal Train SimultaneouslyLearning Rate: 0.008 Epochs: 1,000 each Generator optmiz: Adam Discriminator optimiz: SGD Decay Rate: 5e-5 Momentum: 0.9
- 74. 5:1 Update G D 1 Latent Variables (add noise) Fake Training Data Real MLP MLP Train 1 time Learning Rate: 0.008 Epochs: 1,000 each Generator optmiz: Adam Discriminator optimiz: SGD Decay Rate: 5e-5 Momentum: 0.9 Accuracy Training Set: .91 Accuracy Test Set: .91 Train 5 times
- 75. 1:5 Update G D 1 Latent Variables (add noise) Fake Training Data Real MLP MLP Train 5 times Learning Rate: 0.008 Epochs: 1,000 each Generator optmiz: Adam Discriminator optimiz: SGD Decay Rate: 5e-5 Momentum: 0.9 Accuracy Training Set: .92 Accuracy Test Set: .91 Train 1 time
- 76. Cooperation G D 1 Latent Variables (add noise) Fake MLP MLP Train at once Learning Rate: 0.008 Epochs: 1,000 each Generator optmiz: Adam Discriminator optimiz: SGD Decay Rate: 5e-5 Momentum: 0.9 Accuracy Training Set: .99 Accuracy Test Set: .93 Train at once x sigmoid Vanishing gradient Training Data Real
- 77. Random Strategy G D 1 Latent Variables (add noise) Fake MLP MLP Train at chance Learning Rate: 0.008 Epochs: 1,500 Generator optmiz: Adam Discriminator optimiz: SGD Decay Rate: 5e-5 Momentum: 0.9 Accuracy Test Set: .98 Accuracy Test Set: .96 Polarization of Game until Train at chance 𝑁 = ∞ 𝑥 ≈ 0.5 Training Data Real x sigmoid Vanishing gradient Relu
- 78. Tit-For-Tat Strategy G D 1 Latent Variables (add noise) Fake MLP MLP Train with penalty if provides noisy samples Learning Rate: 0.008 Epochs: 1,500 Generator optmiz: Adam Discriminator optimiz: SGD Decay Rate: 5e-5 Momentum: 0.9 𝒏𝒐𝒊𝒔𝒆 = 𝒙 + 𝟎. 𝟏 ∗ 𝒓𝒏𝒅 𝟎, 𝟏 𝒙 = 𝟎 𝝈 = 𝟏 Accuracy Test Set: .96 Accuracy Test Set: .92 Train Training Data Real In GAN training: % noisy samples 𝒏𝒐𝒊𝒔𝒆 = 𝒙 + 𝟎. 𝟒 ∗ 𝒓𝒏𝒅 𝟎, 𝟏 𝒙 = 𝟎 𝝈 = 𝟏 Accuracy Test Set: .92 Accuracy Test Set: .91
- 79. Freeze Layers G D 1 Latent Variables (add noise) Fake Training Data Real MLP MLP Train First Freeze weights Train Second Learning Rate: 0.008 Epochs: 1,000 each Generator optmiz: Adam Discriminator optimiz: SGD Decay Rate: 5e-5 Momentum: 0.9 Accuracy Training Set: .63 Accuracy Test Set: .12 x sigmoid Vanishing gradient Relu * SYNCHRONICITY