Hitchhiker’s Guide to Generative Adversarial Networks (GANs)
summary
Generative Adversarial Networks claim to produce photorealistic samples using a generator and discriminator trained in a min/max game; convolutional architectures (DCGAN), batch normalization and one‑sided label smoothing are practical fixes. Demonstrations include high‑resolution faces, image super-resolution from low‑res inputs, interactive sketch-to-photo edits and text‑to‑image synthesis; common failures are instability and mode collapse. Training stops when the discriminator cannot tell real from fake (≈0.5); discriminators can also serve as useful classifiers.
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chapters (15)
- 0:00Introduction
- 0:53Overview
- 2:00GANs
- 3:47Why study GANs
- 5:03How GANs are structured
- 9:14Wavelet and Markov chains
- 10:22GAN statistics
- 11:35GAN structure
- 20:11DC Gas
- 20:37Example
- 27:28Multiple Gants
- 29:06Stability
- 35:04How the network terminates
- 36:26Discussion
- 39:11Fake News
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