Diverse Image Generation via Self-Conditioned GANs

Overview

Diverse Image Generation via Self-Conditioned GANs

Project | Paper

Diverse Image Generation via Self-Conditioned GANs
Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, Antonio Torralba
MIT, Adobe Research
in CVPR 2020.

Teaser

Our proposed self-conditioned GAN model learns to perform clustering and image synthesis simultaneously. The model training requires no manual annotation of object classes. Here, we visualize several discovered clusters for both Places365 (top) and ImageNet (bottom). For each cluster, we show both real images and the generated samples conditioned on the cluster index.

Getting Started

Installation

  • Clone this repo:
git clone https://github.com/stevliu/self-conditioned-gan.git
cd self-conditioned-gan
  • Install the dependencies
conda create --name selfcondgan python=3.6
conda activate selfcondgan
conda install --file requirements.txt
conda install -c conda-forge tensorboardx

Training and Evaluation

  • Train a model on CIFAR:
python train.py configs/cifar/selfcondgan.yaml
  • Visualize samples and inferred clusters:
python visualize_clusters.py configs/cifar/selfcondgan.yaml --show_clusters

The samples and clusters will be saved to output/cifar/selfcondgan/clusters. If this directory lies on an Apache server, you can open the URL to output/cifar/selfcondgan/clusters/+lightbox.html in the browser and visualize all samples and clusters in one webpage.

  • Evaluate the model's FID: You will need to first gather a set of ground truth train set images to compute metrics against.
python utils/get_gt_imgs.py --cifar
python metrics.py configs/cifar/selfcondgan.yaml --fid --every -1

You can also evaluate with other metrics by appending additional flags, such as Inception Score (--inception), the number of covered modes + reverse-KL divergence (--modes), and cluster metrics (--cluster_metrics).

Pretrained Models

You can load and evaluate pretrained models on ImageNet and Places. If you have access to ImageNet or Places directories, first fill in paths to your ImageNet and/or Places dataset directories in configs/imagenet/default.yaml and configs/places/default.yaml respectively. You can use the following config files with the evaluation scripts, and the code will automatically download the appropriate models.

configs/pretrained/imagenet/selfcondgan.yaml
configs/pretrained/places/selfcondgan.yaml

configs/pretrained/imagenet/conditional.yaml
configs/pretrained/places/conditional.yaml

configs/pretrained/imagenet/baseline.yaml
configs/pretrained/places/baseline.yaml

Evaluation

Visualizations

To visualize generated samples and inferred clusters, run

python visualize_clusters.py config-file

You can set the flag --show_clusters to also visualize the real inferred clusters, but this requires that you have a path to training set images.

Metrics

To obtain generation metrics, fill in paths to your ImageNet or Places dataset directories in utils/get_gt_imgs.py and then run

python utils/get_gt_imgs.py --imagenet --places

to precompute batches of GT images for FID/FSD evaluation.

Then, you can use

python metrics.py config-file

with the appropriate flags compute the FID (--fid), FSD (--fsd), IS (--inception), number of modes covered/ reverse-KL divergence (--modes) and clustering metrics (--cluster_metrics) for each of the checkpoints.

Training models

To train a model, set up a configuration file (examples in /configs), and run

python train.py config-file

An example config of self-conditioned GAN on ImageNet is config/imagenet/selfcondgan.yaml and on Places is config/places/selfcondgan.yaml.

Some models may be too large to fit on one GPU, so you may want to add --devices DEVICE_NUMBERS as an additional flag to do multi GPU training.

2D-experiments

For synthetic dataset experiments, first go into the 2d_mix directory.

To train a self-conditioned GAN on the 2D-ring and 2D-grid dataset, run

python train.py --clusterer selfcondgan --data_type ring
python train.py --clusterer selfcondgan --data_type grid

You can test several other configurations via the command line arguments.

Acknowledgments

This code is heavily based on the GAN-stability code base. Our FSD code is taken from the GANseeing work. To compute inception score, we use the code provided from Shichang Tang. To compute FID, we use the code provided from TTUR. We also use pretrained classifiers given by the pytorch-playground.

We thank all the authors for their useful code.

Citation

If you use this code for your research, please cite the following work.

@inproceedings{liu2020selfconditioned,
 title={Diverse Image Generation via Self-Conditioned GANs},
 author={Liu, Steven and Wang, Tongzhou and Bau, David and Zhu, Jun-Yan and Torralba, Antonio},
 booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
 year={2020}
}
Implementation of ICCV2021(Oral) paper - VMNet: Voxel-Mesh Network for Geodesic-aware 3D Semantic Segmentation

VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation Created by Zeyu HU Introduction This work is based on our paper VMNet: Voxel-Mes

HU Zeyu 82 Dec 27, 2022
Video-face-extractor - Video face extractor with Python

Python face extractor Setup Create the srcvideos and faces directories Put your

2 Feb 03, 2022
PSPNet in Chainer

PSPNet This is an unofficial implementation of Pyramid Scene Parsing Network (PSPNet) in Chainer. Training Requirement Python 3.4.4+ Chainer 3.0.0b1+

Shunta Saito 76 Dec 12, 2022
A universal framework for learning timestamp-level representations of time series

TS2Vec This repository contains the official implementation for the paper Learning Timestamp-Level Representations for Time Series with Hierarchical C

Zhihan Yue 284 Dec 30, 2022
natural image generation using ConvNets

The Eyescream Project Generating Natural Images using Neural Networks. For our research summary on this work, please read the Arxiv paper: http://arxi

Meta Archive 601 Nov 23, 2022
🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

Monitor deep learning model training and hardware usage from mobile. 🔥 Features Monitor running experiments from mobile phone (or laptop) Monitor har

labml.ai 1.2k Dec 25, 2022
This repository contains PyTorch models for SpecTr (Spectral Transformer).

SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation This repository contains PyTorch models for SpecTr (Spectral Transformer).

Boxiang Yun 45 Dec 13, 2022
A benchmark for the task of translation suggestion

WeTS: A Benchmark for Translation Suggestion Translation Suggestion (TS), which provides alternatives for specific words or phrases given the entire d

zhyang 55 Dec 24, 2022
Orbivator AI - To Determine which features of data (measurements) are most important for diagnosing breast cancer and find out if breast cancer occurs or not.

Orbivator_AI Breast Cancer Wisconsin (Diagnostic) GOAL To Determine which features of data (measurements) are most important for diagnosing breast can

anurag kumar singh 1 Jan 02, 2022
Code for our ICASSP 2021 paper: SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

SA-Net: Shuffle Attention for Deep Convolutional Neural Networks (paper) By Qing-Long Zhang and Yu-Bin Yang [State Key Laboratory for Novel Software T

Qing-Long Zhang 199 Jan 08, 2023
A Pytorch Implementation for Compact Bilinear Pooling.

CompactBilinearPooling-Pytorch A Pytorch Implementation for Compact Bilinear Pooling. Adapted from tensorflow_compact_bilinear_pooling Prerequisites I

169 Dec 23, 2022
Code for Environment Dynamics Decomposition (ED2).

ED2 Code for Environment Dynamics Decomposition (ED2). Installation Follow the installation in MBPO and Dreamer. Usage First follow the SD2 method for

0 Aug 10, 2021
Computer-Vision-Paper-Reviews - Computer Vision Paper Reviews with Key Summary along Papers & Codes

Computer-Vision-Paper-Reviews Computer Vision Paper Reviews with Key Summary along Papers & Codes. Jonathan Choi 2021 50+ Papers across Computer Visio

Jonathan Choi 2 Mar 17, 2022
A simple log parser and summariser for IIS web server logs

IISLogFileParser A basic parser tool for IIS Logs which summarises findings from the log file. Inspired by the Gist https://gist.github.com/wh13371/e7

2 Mar 26, 2022
Caffe-like explicit model constructor. C(onfig)Model

cmodel Caffe-like explicit model constructor. C(onfig)Model Installation pip install git+https://github.com/bonlime/cmodel Usage In order to allow usi

1 Feb 18, 2022
Code and data (Incidents Dataset) for ECCV 2020 Paper "Detecting natural disasters, damage, and incidents in the wild".

Incidents Dataset See the following pages for more details: Project page: IncidentsDataset.csail.mit.edu. ECCV 2020 Paper "Detecting natural disasters

Ethan Weber 67 Dec 27, 2022
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~

YOLOv5-Lite:lighter, faster and easier to deploy Perform a series of ablation experiments on yolov5 to make it lighter (smaller Flops, lower memory, a

pogg 1.5k Jan 05, 2023
[内测中]前向式Python环境快捷封装工具,快速将Python打包为EXE并添加CUDA、NoAVX等支持。

QPT - Quick packaging tool 快捷封装工具 GitHub主页 | Gitee主页 QPT是一款可以“模拟”开发环境的多功能封装工具,最短只需一行命令即可将普通的Python脚本打包成EXE可执行程序,并选择性添加CUDA和NoAVX的支持,尽可能兼容更多的用户环境。 感觉还可

QPT Family 545 Dec 28, 2022
Code for Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022)

Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022) We consider how a user of a web servi

joisino 20 Aug 21, 2022
KakaoBrain KoGPT (Korean Generative Pre-trained Transformer)

KoGPT KoGPT (Korean Generative Pre-trained Transformer) https://github.com/kakaobrain/kogpt https://huggingface.co/kakaobrain/kogpt Model Descriptions

Kakao Brain 799 Dec 28, 2022