CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation

Last update: Apr 19, 2022

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Overview

CDGAN

CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation

CDGAN Implementation in PyTorch

This is the implementation of our paper called "CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation". https://arxiv.org/abs/2001.05489.

CDGAN Architectue

Acknowledgments

We gratefully acknowlege the NVIDIA Corp. for donating us the NVIDIA GeForce Titan X Pascal GPU used for this research.

code is heavily borrowed from CycleGAN Paper pytorch-CycleGAN.

Owner

Kancharagunta Kishan Babu

PhD Student, Computer Vision Group

GitHub Repository

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CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformation

Related tags

Overview

CDGAN

CDGAN Implementation in PyTorch

CDGAN Architectue

Acknowledgments

Owner

Kancharagunta Kishan Babu

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

Torch-ngp - A pytorch implementation of the hash encoder proposed in instant-ngp

Unsupervised Discovery of Object Radiance Fields

Repo 4 basic seminar §How to make human machine readable"

MGFN: Multi-Graph Fusion Networks for Urban Region Embedding was accepted by IJCAI-2022.

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

Flower classification model that classifies flowers in 10 classes made using transfer learning (~85% accuracy).

[NeurIPS 2021] “Improving Contrastive Learning on Imbalanced Data via Open-World Sampling”,

Study of human inductive biases in CNNs and Transformers.

The official repo of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration

[NeurIPS 2020] Official Implementation: "SMYRF: Efficient Attention using Asymmetric Clustering".

My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs (GNN, GAT, GraphSAGE, GCN)

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