TransGAN: Two Transformers Can Make One Strong GAN

Last update: Jan 07, 2023

Overview

TransGAN: Two Transformers Can Make One Strong GAN

Code used for TransGAN: Two Transformers Can Make One Strong GAN.

Main Pipeline

Visual Results

prepare fid statistic file

mkdir fid_stat

Download the pre-calculated statistics (Google Drive) to ./fid_stat.

Environment

pip install -r requirements.txt

Notice: Pytorch version has to be <=1.3.0 !

Training

Coming soon

Testing

Firstly download the checkpoint from (Google Drive) to ./pretrained_weight

# cifar-10
sh exps/cifar10_test.sh

# stl-10
sh exps/stl10_test.sh

Acknowledgement

FID code and CIFAR-10 statistics file from https://github.com/bioinf-jku/TTUR (official). Codebase from AutoGAN

Owner

VITA

Visual Informatics Group @ University of Texas at Austin

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TransGAN: Two Transformers Can Make One Strong GAN

Related tags

Overview

TransGAN: Two Transformers Can Make One Strong GAN

Main Pipeline

Visual Results

prepare fid statistic file

Environment

Training

Testing

Acknowledgement

Owner

VITA

CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

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This project uses ViT to perform image classification tasks on DATA set CIFAR10.

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Codes and Data Processing Files for our paper.

TransGAN: Two Transformers Can Make One Strong GAN

Related tags

Overview

TransGAN: Two Transformers Can Make One Strong GAN

Main Pipeline

Visual Results

prepare fid statistic file

Environment

Training

Testing

Acknowledgement

Owner

VITA

CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

The official implementation of our CVPR 2021 paper - Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach

Adaptive Dropblock Enhanced GenerativeAdversarial Networks for Hyperspectral Image Classification

Code for our CVPR 2021 Paper "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes".

Harmonious Textual Layout Generation over Natural Images via Deep Aesthetics Learning

Make a surveillance camera from your raspberry pi!

Train a state-of-the-art yolov3 object detector from scratch!

Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation

This project uses ViT to perform image classification tasks on DATA set CIFAR10.

Official Implement of CVPR 2021 paper “Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting”

Code for the paper "Adapting Monolingual Models: Data can be Scarce when Language Similarity is High"

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

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PyDeepFakeDet is an integrated and scalable tool for Deepfake detection.

This is the code for ACL2021 paper A Unified Generative Framework for Aspect-Based Sentiment Analysis

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STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech

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PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)

Codes and Data Processing Files for our paper.

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long, Evan Shelhamer, and Trevor Darrell. CVPR 2015 and PAMI 2016.