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"

Cuong Nguyen

Last update: Nov 15, 2021

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

Run 0_prepare_dataset.py to automatically download, process and store images of CIFAR10, STL10, MNIST datasets. CAT dataset can be downloaded here.
Download FID stats for CIFAR-10 at http://bioinf.jku.at/research/ttur/ttur_stats/fid_stats_cifar10_train.npz

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])

Comments

Error parsing message with type 'tensorflow.GraphDef'

Use tf.gfile.GFile. Traceback (most recent call last): File "train.py", line 563, in metric_fid.create_inception_graph(str(param.dir_inception)) File "/media/sang/UBUNTU/CLASSIFICATION/RMCosGAN-master/metric_fid.py", line 39, in create_inception_graph graph_def.ParseFromString( f.read()) google.protobuf.message.DecodeError: Error parsing message with type 'tensorflow.GraphDef'

opened by doansangg 0

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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

Overview

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

Requirement

Data preparation

Training

Appendix

Network Architectures

Experimental results

Citation

Contact

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