1 Variational Autoencoder
Read sections 2 and 3 in https://arxiv.org/pdf/1312.6114.pdf for implementation details. Specifically, you need to inplement the reparameterization trick, and use equation (10) as loss term.
Fill the blank parts in ./vae/model vae.py. After training, show the reconstructed images and sampled images. If tuning network structure carefully, you should get good results after 30 epoches.
2 Generative Adversarial Network
Read section 3 in https://papers.nips.cc/paper/5423-generative-adversarial-nets. pdf for implementation details. https://arxiv.org/pdf/1511.06434.pdf contains more tricks about how to define your network structure.
Fill the blank parts in ./gan/model gan.py. After training, show the sampled images. You should also get good results after 30 epoches.
