CPC

Implementation of Representation Learning with Contrastive Predictive Coding Paper (https://arxiv.org/abs/1807.03748).

Simple Test on MNIST

We Split MNIST images into 3x3 grid, composed of 9, 14x14 images with 50% overlap.

For the autoregressive part we use GRU cell and we predict 5 steps in the future. We use Negative samples from other images of the batch.

Usage

• The Encoder weights are saved after each train using the train.py file.
• We train a classifier with one hidden layer on top of the features spaces of the freezed Encoder in classifier.py file.
• The classifier reach 85% accuracy on Mnist.

ToDo :

• Make the contrastive predicition row wise.
• Train on cifar-10