Picture generation — GAN of the week
GAN of the Week is a series of notes about Generative Models, including GANs and Autoencoders. Every week I’ll review a new model to help you keep up with these rapidly developing types of Neural Networks.
This week GAN of the week is a Deep Convolutional GAN
Long story short Deep Convolutional GAN (DCGAN) is roughly same as Vanilla GAN but it contains convolutional layers, which is made more suitable for extracting important features from the pictures and therefore it is more suitable for picture generation.
DCGAN use advantages of convolutional neural networks (CNN), combining advantages from both worlds — good image representations that could be obtained by GAN, and reusing parts of the generator and discriminator networks as feature extractors for supervised tasks.

Results:
First of all, I started to use PyTorch (go PyTotch!) and it feels awesome, I highly recommend this framework for everyone.
I made DCGAN implementation with PyTorch, the code can be found on my GitHub. In order to improve stability, you can try to play with hyperparameters that can be found in config.toml.


I’m pretty pleased with the results, although they can be further improved by tweaking hyperparameters.
References:
DCGAN original paper — https://arxiv.org/pdf/1511.06434.pdf
My PyTorch DCGAN implementation — https://github.com/subpath/DCGAN_with_Pytorch
Have you tried DCGAN for picture generation?
Previous GAN of the week articles: https://medium.com/@subpath/full-list-of-publications-gan-of-the-week-817a02d859ee

