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GAN and Quantum
- 1. GAN Rajesh Jeyapaul Sr. Developer Advocate and AI Architect , IBM India
- 2. Fake vs Real
- 3. ICLR2019 talk by Ian GoodFellow
- 4. ICLR2019 talk by Ian GoodFellow
- 5. ML vs Adversarial • Value function – not cost function • Min-max • Player1 wants to mimimise the winning option of player2 , player2 want to maximize the winning option • Player 1 looking for local minima , player 2 looking for local maxima • Nash equllibrium • Each player is assumed to know the equilibrium strategies of the other players
- 8. ICLR2019 talk by Ian GoodFellow Progressive GAN 2 representative models created from celebA dataset Take the sample and learn the probability distribution- generate new samples from the same distribution • Statistically same as the personalities in the training data
- 9. How GAN works – 2 player minimax game •
- 10. 2 player minimax game • Player 1 – Generator creates images • Player 2 - Discriminator – recognizes the input as real or fake • Adversarial competition on how to classify the fake samples generated by generator . Generator tries to adapt the input to discriminator to cause it to be misclassified. Discriminator tries to correctly classify fake as fake / real as real • NASH Equllibrium • Generator recovers the data distribution correctly • Discriminator random guess whether the input is real or fake • Practically reaching NASH equilibrium is not possible but we have reached to a place where we can generate realistic samples
- 12. Generating face is relatively easy – BIG GAN made it possible with imagenet
- 13. Advantage – able to learn with less supervised • Converting day scene to night scene • Unsupervised image to image translation
- 18. Challenges – Instability during training
- 19. Challenges – mode collpase
- 20. QUANTUM GAN • DENSITY MATRIX – STATES (possible configuration of Q system • Quantum distribution • Quantum super imposition vs classical uncertainity (undo is possible)
- 22. Quantum generative adversarial learning • https://arxiv.org/abs/1804.09139