Tutorial on Deep Learning IV
summary
Covers how GANs as a min/max game learn a generator and differentiable discriminator that sample (no explicit density), showing deconvolution GAN images on bedrooms, CIFAR and ImageNet but raising memorization concerns; details how undirected models like RBMs need partition-function estimates so annealed importance sampling gives unbiased Z but underestimates log Z, and a reverse-AIS/unrolling trick provides a conservative bound (MNIST experiments show a substantial gap if AIS is too short); also shows sequence-to-sequence sentence encoders trained on 11,000 books that match hand‑engineered features and multi‑game deep RL transfer across Atari titles.
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chapters (11)
- 0:00Introduction to GANs
- 3:24GAN Architecture
- 6:35GAN Applications and Issues
- 12:04Model Evaluation Challenges
- 17:31Annealed Importance Sampling
- 24:21Reverse Annealing Model
- 30:32Evaluation Techniques
- 43:02Generative Model Applications
- 44:45Natural Language Generation
- 51:17Reading Comprehension
- 57:55Reinforcement Learning
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