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

GitHub Repository

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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

[ACM MM 2021] Diverse Image Inpainting with Bidirectional and Autoregressive Transformers

[CVPR 21] Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

A high-performance distributed deep learning system targeting large-scale and automated distributed training.

CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

Lightweight mmm - Lightweight (Bayesian) Media Mix Model

Paddle implementation for "Highly Efficient Knowledge Graph Embedding Learning with Closed-Form Orthogonal Procrustes Analysis" (NAACL 2021)

Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut

VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning

Experiment about Deep Person Re-identification with EfficientNet-v2

Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

Additional code for Stable-baselines3 to load and upload models from the Hub.

AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

SeisComP/SeisBench interface to enable deep-learning (re)picking in SeisComP

Single object tracking and segmentation.

Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

Implementation of QuickDraw - an online game developed by Google, combined with AirGesture - a simple gesture recognition application

A framework for GPU based high-performance medical image processing and visualization

This is the repository for Learning to Generate Piano Music With Sustain Pedals

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

This is the implementation of the paper "Self-supervised Outdoor Scene Relighting"