09 – AE, DAE, and VAE with PyTorch; generative adversarial networks (GAN) and code
Channel: Alfredo Canziani
Date: 19/05/2021

Course website: http://bit.ly/DLSP21-web
Playlist: http://bit.ly/DLSP21-YouTube
Speaker: Alfredo Canziani

Chapters
00:00 – 1st of April 2021
03:24 – Training an autoencoder (AE) (PyTorch and Notebook)
11:34 – Looking at an AE kernels
15:41 – Denoising autoencoder (recap)
17:33 – Training a denoising autoencoder (DAE) (PyTorch and Notebook)
20:59 – Looking at a DAE kernels
22:57 – Comparison with state of the art inpainting techniques
24:34 – AE as an EBM
26:23 – Training a variational autoencoder (VAE) (PyTorch and Notebook)
36:24 – A VAE as a generative model
37:30 – Interpolation in input and latent space
39:02 – A VAE as an EBM
39:23 – VAE embeddings distribution during training
42:58 – Generative adversarial networks (GANs) vs. DAE
45:43 – Generative adversarial networks (GANs) vs. VAE
47:11 – Training a GAN, the cost network
50:08 – Training a GAN, the generating network
51:34 – A possible cost network's architecture
54:33 – The Italian vs. Swiss analogy for GANs
59:13 – Training a GAN (PyTorch code reading)
1:06:09 – That was it :D