This repository contains all source code, pre-trained models related to the paper "An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator"

Related tags

Deep Learningpytorchgenerative-adversarial-networkloss-functionsmargin-cosine-loss
Overview

An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator

This is a Pytorch implementation for the paper "An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator".

Requirement

  • python 3.7.3
  • pytorch 1.2.0
  • tensorflow 2.0.0
  • torchtext 0.4.0
  • torchvision 0.4.0
  • mnist

Data preparation

Training

  • Run 1_train.sh to train our proposed loss function RMCosGAN along with other loss functions on four datasets.

Appendix

Network Architectures

DCGAN Architecture for CIFAR-10, MNIST and STL-10 datasets

Operation Filter Units Non Linearity Normalization
Generator G(z)
Linear 512 None None
Trans.Conv2D 256 ReLU Batch
Trans.Conv2D 128 ReLU Batch
Trans.Conv2D 64 ReLU Batch
Trans.Conv2D 3 Tanh None
Discriminator D(x)
Conv2D 64 Leaky-ReLU Spectral
Conv2D 64 Leaky-ReLU Spectral
Conv2D 128 Leaky-ReLU Spectral
Conv2D 128 Leaky-ReLU Spectral
Conv2D 256 Leaky-ReLU Spectral
Conv2D 256 Leaky-ReLU Spectral
Conv2D 512 Leaky-ReLU Spectral

DCGAN Architecture for CAT dataset

Operation Filter Units Non Linearity Normalization
Generator G(z)
Trans.Conv2D 1024 ReLU Batch
Trans.Conv2D 512 ReLU Batch
Trans.Conv2D 256 ReLU Batch
Trans.Conv2D 128 ReLU Batch
Trans.Conv2D 3 Tanh None
Discriminator D(x)
Conv2D 128 Leaky-ReLU Spectral
Conv2D 256 Leaky-ReLU Spectral
Conv2D 512 Leaky-ReLU Spectral
Conv2D 1024 Leaky-ReLU Spectral

Experimental results

60 randomly-generated images with RMCosGAN at FID=31.34 trained on CIFAR-10 dataset

60 randomly-generated images with RMCosGAN at FID=13.17 trained on MNIST dataset

60 randomly-generated images with RMCosGAN FID=52.16 trained on STL-10 dataset

60 randomly-generated images with RMCosGAN at FID=9.48 trained on CAT dataset

Citation

Please cite our paper if RMCosGAN is used:

@article{RMCosGAN,
  title={An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator},
  author={Cuong Nguyen, Tien-Dung Cao, Tram Truong-Huu, Binh T.Nguyen},
  journal={},
  year={}
}

If this implementation is useful, please cite or acknowledge this repository on your work.

Contact

Cuong Nguyen ([email protected]),

Tien-Dung Cao ([email protected]),

Tram Truong-Huu ([email protected]),

Binh T.Nguyen ([email protected])

Owner
Cuong Nguyen
AI/DL researcher
Cuong Nguyen
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