GAN and Quantum
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This document discusses generative adversarial networks (GANs) and their training process. It explains that GANs involve a minimax game between a generator and discriminator, where the generator tries to generate fake images that cannot be distinguished from real images by the discriminator. The goal is for them to reach a Nash equilibrium where the generator recovers the real data distribution and the discriminator guesses at random. The document also mentions applications of GANs like image-to-image translation and challenges such as instability during training and mode collapse. Finally, it discusses the idea of quantum GANs that use quantum states and distributions instead of classical ones.
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Generative Adversarial Networks (GANs) are composed of two neural networks, a generator and a discriminator, that compete against each other. The generator tries to generate fake images that look real, while the discriminator tries to distinguish real images from fake ones. Over time, this adversarial training helps improve the generator at producing more realistic images. However, training GANs can be difficult due to issues like mode collapse. New techniques like progressive growing, minibatch discrimination, and equalized learning rates help address these issues and generate higher quality images.
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How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Public CyberSecurity Awareness Presentation 2024.pptx
Public CyberSecurity Awareness Presentation 2024.pptx
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