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"

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])

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

Owner

Cuong Nguyen

Minimal But Practical Image Classifier Pipline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.

PyTorch Implementation of Sparse DETR

Lepard: Learning Partial point cloud matching in Rigid and Deformable scenes

Pytorch re-implementation of Paper: SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition (CVPR 2022)

Python lib to talk to pylontech lithium batteries (US2000, US3000, ...) using RS485

KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

Collection of tasks for fast prototyping, baselining, finetuning and solving problems with deep learning.

PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.

A curated list of awesome Machine Learning frameworks, libraries and software.

GLIP: Grounded Language-Image Pre-training

Adversarial Self-Defense for Cycle-Consistent GANs

Vanilla and Prototypical Networks with Random Weights for image classification on Omniglot and mini-ImageNet. Made with Python3.

CLEAR algorithm for multi-view data association

[NeurIPS 2021] A weak-shot object detection approach by transferring semantic similarity and mask prior.

Exploring the Dual-task Correlation for Pose Guided Person Image Generation

Supervised & unsupervised machine-learning techniques are applied to the database of weighted P4s which admit Calabi-Yau hypersurfaces.

Code for EMNLP2020 long paper: BERT-Attack: Adversarial Attack Against BERT Using BERT

Pytorch implementation of the paper: "SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Image Deraining"

Tracking code for the winner of track 1 in the MMP-Tracking Challenge at ICCV 2021 Workshop.

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

Owner

Cuong Nguyen

Minimal But Practical Image Classifier Pipline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.

PyTorch Implementation of Sparse DETR

Lepard: Learning Partial point cloud matching in Rigid and Deformable scenes

Pytorch re-implementation of Paper: SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition (CVPR 2022)

Python lib to talk to pylontech lithium batteries (US2000, US3000, ...) using RS485

KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

Collection of tasks for fast prototyping, baselining, finetuning and solving problems with deep learning.

PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

A curated list of awesome Machine Learning frameworks, libraries and software.

GLIP: Grounded Language-Image Pre-training

Adversarial Self-Defense for Cycle-Consistent GANs

Vanilla and Prototypical Networks with Random Weights for image classification on Omniglot and mini-ImageNet. Made with Python3.

CLEAR algorithm for multi-view data association

[NeurIPS 2021] A weak-shot object detection approach by transferring semantic similarity and mask prior.

Exploring the Dual-task Correlation for Pose Guided Person Image Generation

Supervised & unsupervised machine-learning techniques are applied to the database of weighted P4s which admit Calabi-Yau hypersurfaces.

Code for EMNLP2020 long paper: BERT-Attack: Adversarial Attack Against BERT Using BERT

Pytorch implementation of the paper: "SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Image Deraining"

Tracking code for the winner of track 1 in the MMP-Tracking Challenge at ICCV 2021 Workshop.

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.