Learning What and Where to Draw

Related tags

Deep Learningnips2016
Overview

###Learning What and Where to Draw Scott Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee

This is the code for our NIPS 2016 paper on text- and location-controllable image synthesis using conditional GANs. Much of the code is adapted from reedscot/icml2016 and dcgan.torch.

####Setup Instructions

You will need to install Torch, CuDNN, stnbhwd and the display package.

####How to train a text to image model:

  1. Download the data including captions, location annotations and pretrained models.
  2. Download the birds and humans image data.
  3. Modify the CONFIG file to point to your data.
  4. Run one of the training scripts, e.g. ./scripts/train_cub_keypoints.sh

####How to generate samples:

  • ./scripts/run_all_demos.sh.
  • html files will be generated with results like the following:

Moving the bird's position via bounding box:

Moving the bird's position via keypoints:

Birds text to image with ground-truth keypoints:

Birds text to image with generated keypoints:

Humans text to image with ground-truth keypoints:

Humans text to image with generated keypoints:

####Citation

If you find this useful, please cite our work as follows:

@inproceedings{reed2016learning,
  title={Learning What and Where to Draw},
  author={Scott Reed and Zeynep Akata and Santosh Mohan and Samuel Tenka and Bernt Schiele and Honglak Lee},
  booktitle={Advances in Neural Information Processing Systems},
  year={2016}
}
Owner
Scott Ellison Reed
Research Scientist
Scott Ellison Reed
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

Holy Wu 35 Jan 01, 2023
A simple configurable bot for sending arXiv article alert by mail

arXiv-newsletter A simple configurable bot for sending arXiv article alert by mail. Prerequisites PyYAML=5.3.1 arxiv=1.4.0 Configuration All config

SXKDZ 21 Nov 09, 2022
codes for Self-paced Deep Regression Forests with Consideration on Ranking Fairness

Self-paced Deep Regression Forests with Consideration on Ranking Fairness This is official codes for paper Self-paced Deep Regression Forests with Con

Learning in Vision 4 Sep 11, 2022
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
DataCLUE: 国内首个以数据为中心的AI测评(含模型分析报告)

DataCLUE: A Benchmark Suite for Data-centric NLP You can get the english version of README. 以数据为中心的AI测评(DataCLUE) 内容导引 章节 描述 简介 介绍以数据为中心的AI测评(DataCLUE

CLUE benchmark 135 Dec 22, 2022
Code Repo for the ACL21 paper "Common Sense Beyond English: Evaluating and Improving Multilingual LMs for Commonsense Reasoning"

Common Sense Beyond English: Evaluating and Improving Multilingual LMs for Commonsense Reasoning This is the Github repository of our paper, "Common S

INK Lab @ USC 19 Nov 30, 2022
EfficientMPC - Efficient Model Predictive Control Implementation

efficientMPC Efficient Model Predictive Control Implementation The original algo

Vin 8 Dec 04, 2022
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).

MixHop and N-GCN ⠀ A PyTorch implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019)

Benedek Rozemberczki 393 Dec 13, 2022
Vector AI — A platform for building vector based applications. Encode, query and analyse data using vectors.

Vector AI is a framework designed to make the process of building production grade vector based applications as quickly and easily as possible. Create

Vector AI 267 Dec 23, 2022
An Open Source Machine Learning Framework for Everyone

Documentation TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, a

170.1k Jan 05, 2023
PyTorch implementation for the ICLR 2020 paper "Understanding the Limitations of Variational Mutual Information Estimators"

Smoothed Mutual Information ``Lower Bound'' Estimator PyTorch implementation for the ICLR 2020 paper Understanding the Limitations of Variational Mutu

50 Nov 09, 2022
Code for "Learning Graph Cellular Automata"

Learning Graph Cellular Automata This code implements the experiments from the NeurIPS 2021 paper: "Learning Graph Cellular Automata" Daniele Grattaro

Daniele Grattarola 37 Oct 26, 2022
Unsupervised Image-to-Image Translation

UNIT: UNsupervised Image-to-image Translation Networks Imaginaire Repository We have a reimplementation of the UNIT method that is more performant. It

Ming-Yu Liu 劉洺堉 1.9k Dec 26, 2022
Data for "Driving the Herd: Search Engines as Content Influencers" paper

herding_data Data for "Driving the Herd: Search Engines as Content Influencers" paper Dataset description The collection contains 2250 documents, 30 i

0 Aug 17, 2021
[NeurIPS 2021 Spotlight] Code for Learning to Compose Visual Relations

Learning to Compose Visual Relations This is the pytorch codebase for the NeurIPS 2021 Spotlight paper Learning to Compose Visual Relations. Demo Imag

Nan Liu 88 Jan 04, 2023
LSSY量化交易系统

LSSY量化交易系统 该项目是本人3年来研究量化慢慢积累开发的一套系统,属于早期作品慢慢修改而来,仅供学习研究,回测分析,实盘交易部分未公开

55 Oct 04, 2022
DANet for Tabular data classification/ regression.

Deep Abstract Networks A PyTorch code implemented for the submission DANets: Deep Abstract Networks for Tabular Data Classification and Regression. Do

Ronnie Rocket 55 Sep 14, 2022
Self-Supervised Image Denoising via Iterative Data Refinement

Self-Supervised Image Denoising via Iterative Data Refinement Yi Zhang1, Dasong Li1, Ka Lung Law2, Xiaogang Wang1, Hongwei Qin2, Hongsheng Li1 1CUHK-S

Zhang Yi 72 Jan 01, 2023
Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"

RandWireNN Unofficial PyTorch Implementation of: Exploring Randomly Wired Neural Networks for Image Recognition. Results Validation result on Imagenet

Seung-won Park 684 Nov 02, 2022
Degree-Quant: Quantization-Aware Training for Graph Neural Networks.

Degree-Quant This repo provides a clean re-implementation of the code associated with the paper Degree-Quant: Quantization-Aware Training for Graph Ne

35 Oct 07, 2022