Completed
– 1st of April 2021
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Classroom Contents
AE, DAE, and VAE with PyTorch - Generative Adversarial Networks and Code
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- 1 – 1st of April 2021
- 2 – Training an autoencoder AE PyTorch and Notebook
- 3 – Looking at an AE kernels
- 4 – Denoising autoencoder recap
- 5 – Training a denoising autoencoder DAE PyTorch and Notebook
- 6 – Looking at a DAE kernels
- 7 – Comparison with state of the art inpainting techniques
- 8 – AE as an EBM
- 9 – Training a variational autoencoder VAE PyTorch and Notebook
- 10 – A VAE as a generative model
- 11 – Interpolation in input and latent space
- 12 – A VAE as an EBM
- 13 – VAE embeddings distribution during training
- 14 – Generative adversarial networks GANs vs. DAE
- 15 – Generative adversarial networks GANs vs. VAE
- 16 – Training a GAN, the cost network
- 17 – Training a GAN, the generating network
- 18 – A possible cost network's architecture
- 19 – The Italian vs. Swiss analogy for GANs
- 20 – Training a GAN PyTorch code reading
- 21 – That was it :D
