Create README.md

Author: ritheshkumar95

README.md

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+## Reproducing Neural Discrete Representation Learning
+### Course Project for [IFT 6135 - Representation Learning](https://ift6135h18.wordpress.com/)
+
+Project Report link: [final_project.pdf](final_project.pdf)
+
+### Instructions
+1. To train the VQVAE with default arguments as discussed in the report, execute:
+```
+python vqvae.py --data-folder /tmp/miniimagenet --output-folder models/vqvae
+```
+2. To train the PixelCNN prior on the latents, execute:
+```
+python pixelcnn_prior.py --data-folder /tmp/miniimagenet --model models/vqvae --output-folder models/pixelcnn_prior
+```
+
+### Reconstructions from VQ-VAE
+Top 4 rows are Original Images. Bottom 4 rows are Reconstructions.
+#### MNIST
+![png](samples/vqvae_reconstructions_MNIST.png)
+#### Fashion MNIST
+![png](samples/vqvae_reconstructions_FashionMNIST.png)
+
+### Class-conditional samples from VQVAE with PixelCNN prior on the latents
+#### MNIST
+![png](samples/samples_MNIST.png)
+#### Fashion MNIST
+![png](samples/samples_FashionMNIST.png)
+
+### Comments
+1. We noticed that implementing our own VectorQuantization PyTorch function speeded-up training of VQ-VAE by nearly 3x. The slower, but simpler code is in this [commit](https://github.com/ritheshkumar95/pytorch-vqvae/tree/cde142670f701e783f29e9c815f390fc502532e8).
+2. We added some basic tests for the vector quantization functions (based on `pytest`). To run these tests
+```
+py.test . -vv
+```
+
+### Authors
+1. Rithesh Kumar
+2. Tristan Deleu
+3. Evan Racah