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msrf_gan's Introduction

GAN for fashion-MNIST, with FID,Cuda

Attached is a basic GAN, referenced to "MNIST FASION".

Since the discriminator also improves during training, I needed an external tool to evaluate the improvement of the genreted images.

To do this I used FID.

https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance

Following are few pictures to illustrate the training progress.

Generated fake images.

Epoch#0

Generated Real images.

Epoch#3

This GAN suffers from mode-collapse.

By using an Unrolled GAN, we can prevent the generator from optimizing for a single fixed discriminator, since the loss function incorporates not only the current discriminator's classifications, but also future discriminator versions.

So the generator can't over-optimize for a single discriminator.

Getting Started

Clone the repository into a local machine using

git clone https://github.com/anubhavanand1516/MSRF_GAN

Instructions to run

These notebooks were developed on pytorch with cuda implementation, as they provide sufficient GPU resources to develop these models. To open these notebooks locally, run

python Teamx.py

Following is the FID graph:

FID loss vs Epochs

Thanks.

msrf_gan's People

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