GANs. Machine Learning: Jordan Boyd-Graber University of Maryland SLIDES ADAPTED FROM GRAHAM NEUBIG
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1 GANs Machine Learning: Jordan Boyd-Graber University of Maryland SLIDES ADAPTED FROM GRAHAM NEUBIG Machine Learning: Jordan Boyd-Graber UMD GANs 1 / 7
2 Problems with Generation Generative Models Ain t Perfect Over-emphasis of common outputs, fuzziness Real MLE Adversarial Note: this is probably a good idea if you are doing (Lotter et al. maximum 2015) likelihood! Image Credit: Lotter et al Fitting conventional prob models focuses on common input Can be fuzzy Still better for smaller ammounts of data or if true objective is ML Machine Learning: Jordan Boyd-Graber UMD GANs 2 / 7
3 Adversarial Training It s time for some game theory Machine Learning: Jordan Boyd-Graber UMD GANs 3 / 7
4 Adversarial Training It s time for some game theory Create discriminator that criticizes generated output Is this example real or not Generator is trained to fool discriminator to say it s real Machine Learning: Jordan Boyd-Graber UMD GANs 3 / 7
5 Adversarial Training It s time for some game theory Create discriminator that criticizes generated output Is this example real or not Generator is trained to fool discriminator to say it s real Contrast with encoder / decoder: Machine Learning: Jordan Boyd-Graber UMD GANs 3 / 7
6 Adversarial Training It s time for some game theory Create discriminator that criticizes generated output Is this example real or not Generator is trained to fool discriminator to say it s real Contrast with encoder / decoder: no fixed representation Machine Learning: Jordan Boyd-Graber UMD GANs 3 / 7
7 Training GAN Training Method sample latent vars. z sample minibatch convert w/ generator xreal xfake predict w/ discriminator discriminator loss (higher if fail predictions) y generator loss (higher if make predictions) Machine Learning: Jordan Boyd-Graber UMD GANs 4 / 7
8 Training Equations Discriminator l D (θ D,θ G ) = x P data [logd(x)] z [log(1 D(G(z)))] Real data should get high score Fake data should get low score Machine Learning: Jordan Boyd-Graber UMD GANs 5 / 7
9 Training Equations Discriminator l D (θ D,θ G ) = x P data [logd(x)] z [log(1 D(G(z)))] Real data should get high score Fake data should get low score Machine Learning: Jordan Boyd-Graber UMD GANs 5 / 7
10 Training Equations Discriminator l D (θ D,θ G ) = x P data [logd(x)] z [log(1 D(G(z)))] Real data should get high score Fake data should get low score Machine Learning: Jordan Boyd-Graber UMD GANs 5 / 7
11 Training Equations Discriminator Generator l D (θ D,θ G ) = x P data [logd(x)] z [log(1 D(G(z)))] Real data should get high score Fake data should get low score l G (θ D,θ G ) = l D (θ D,θ G ) If discriminator is very accurate, sometimes better to focus on non-saturating loss Focus on where you can confuse discriminator z [ logd(g(z))] (1) Machine Learning: Jordan Boyd-Graber UMD GANs 5 / 7
12 Problems with Training GANs are great, but training very hard Mode Collapse: generator maps all z to single x Over-confident discriminator Machine Learning: Jordan Boyd-Graber UMD GANs 6 / 7
13 Problems with Training GANs are great, but training very hard Mode Collapse: generator maps all z to single x (other examples as side information) Over-confident discriminator Machine Learning: Jordan Boyd-Graber UMD GANs 6 / 7
14 Problems with Training GANs are great, but training very hard Mode Collapse: generator maps all z to single x (other examples as side information) Over-confident discriminator (smoothing) Machine Learning: Jordan Boyd-Graber UMD GANs 6 / 7
15 Problem! Can t Backprop Problems with Discrete Data through Sampling sample latent vars. z sample minibatch convert w/ generator xreal predict w/ discriminator y xfake Discrete! Can t backprop Machine Learning: Jordan Boyd-Graber UMD GANs 7 / 7
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