Self-supervised Label Augmentation via Input Transformations (ICML 2020)

Related tags

Deep LearningSLA
Overview

Self-supervised Label Augmentation via Input Transformations

Install dependencies

conda create -n SLA python=3.7
conda activate SLA
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
conda install ignite -c pytorch
pip install tensorboard

We tested our code on the following versions:

  • pytorch==1.5.1
  • torchvision=0.6.1
  • ignite==0.4.0.post1

Training

We here provide a training script for cifar10.

python train.py \
    --dataset cifar10 --datadir data/ --batchsize 128 --num-iterations 80000 --val-freq 1000 \
    --model cresnet32 \
    --mode sla --aug rotation

For other training objectives, replace the --mode option with baseline, da, mt, or sla+sd. For other augmentations, replace the --aug option with the function names in augmentations.py.

Large-scale datasets. We empirically found that summation (instead of average) of losses across self-supervised transformations could provide an accuracy gain in the large-scale datasets such as ImageNet or iNaturalist. To this end, use the --with-large-loss option.

Evaluation

You can check the results in the log files stored in the logs/ directory (single_acc for SLA+SI or SLA+SD; agg_acc for SLA+AG). To re-evaluation, use test.py.

BibTeX

@inproceedings{lee2020_sla,
  title={Self-supervised label augmentation via input transformations},
  author={Lee, Hankook and Hwang, Sung Ju and Shin, Jinwoo},
  booktitle={International Conference on Machine Learning},
  pages={5714--5724},
  year={2020},
  organization={PMLR}
}}
Owner
hankook
hankook
COPA-SSE contains crowdsourced explanations for the Balanced COPA dataset

COPA-SSE Repository for COPA-SSE: Semi-Structured Explanations for Commonsense Reasoning. COPA-SSE contains crowdsourced explanations for the Balanced

Ana Brassard 5 Jul 31, 2022
Pytorch implementation of Nueral Style transfer

Nueral Style Transfer Pytorch implementation of Nueral style transfer algorithm , it is used to apply artistic styles to content images . Content is t

Abhinav 9 Oct 15, 2022
Code for paper: Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks

Group-CAM By Zhang, Qinglong and Rao, Lu and Yang, Yubin [State Key Laboratory for Novel Software Technology at Nanjing University] This repo is the o

zhql 98 Nov 16, 2022
The official implementation of VAENAR-TTS, a VAE based non-autoregressive TTS model.

VAENAR-TTS This repo contains code accompanying the paper "VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis". Sa

THUHCSI 138 Oct 28, 2022
Prototypical Networks for Few shot Learning in PyTorch

Prototypical Networks for Few shot Learning in PyTorch Simple alternative Implementation of Prototypical Networks for Few Shot Learning (paper, code)

Orobix 835 Jan 08, 2023
Source code of our BMVC 2021 paper: AniFormer: Data-driven 3D Animation with Transformer

AniFormer This is the PyTorch implementation of our BMVC 2021 paper AniFormer: Data-driven 3D Animation with Transformer. Haoyu Chen, Hao Tang, Nicu S

24 Nov 02, 2022
A python package for generating, analyzing and visualizing building shadows

pybdshadow Introduction pybdshadow is a python package for generating, analyzing and visualizing building shadows from large scale building geographic

Qing Yu 13 Nov 30, 2022
PyTorch/GPU re-implementation of the paper Masked Autoencoders Are Scalable Vision Learners

Masked Autoencoders: A PyTorch Implementation This is a PyTorch/GPU re-implementation of the paper Masked Autoencoders Are Scalable Vision Learners: @

Meta Research 4.8k Jan 04, 2023
Repository for the NeurIPS 2021 paper: "Exploiting Domain-Specific Features to Enhance Domain Generalization".

meta-Domain Specific-Domain Invariant (mDSDI) Source code implementation for the paper: Manh-Ha Bui, Toan Tran, Anh Tuan Tran, Dinh Phung. "Exploiting

VinAI Research 12 Nov 25, 2022
FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization

FuseDream This repo contains code for our paper (paper link): FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimizat

XCL 191 Dec 31, 2022
Unified API to facilitate usage of pre-trained "perceptor" models, a la CLIP

mmc installation git clone https://github.com/dmarx/Multi-Modal-Comparators cd 'Multi-Modal-Comparators' pip install poetry poetry build pip install d

David Marx 37 Nov 25, 2022
This is the official implementation of our proposed SwinMR

SwinMR This is the official implementation of our proposed SwinMR: Swin Transformer for Fast MRI Please cite: @article{huang2022swin, title={Swi

A Yang Lab (led by Dr Guang Yang) 27 Nov 17, 2022
Datasets, tools, and benchmarks for representation learning of code.

The CodeSearchNet challenge has been concluded We would like to thank all participants for their submissions and we hope that this challenge provided

GitHub 1.8k Dec 25, 2022
A robust camera and Lidar fusion based velocity estimator to undistort the pointcloud.

Lidar with Velocity A robust camera and Lidar fusion based velocity estimator to undistort the pointcloud. related paper: Lidar with Velocity : Motion

ISEE Research Group 164 Dec 30, 2022
Learning with Subset Stacking

Learning with Subset Stacking (LESS) LESS is a new supervised learning algorithm that is based on training many local estimators on subsets of a given

S. Ilker Birbil 19 Oct 04, 2022
Adversarial Learning for Modeling Human Motion

Adversarial Learning for Modeling Human Motion This repository contains the open source code which reproduces the results for the paper: Adversarial l

wangqi 6 Jun 15, 2021
Code for the Convolutional Vision Transformer (ConViT)

ConViT : Vision Transformers with Convolutional Inductive Biases This repository contains PyTorch code for ConViT. It builds on code from the Data-Eff

Facebook Research 418 Jan 06, 2023
This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

haifeng xia 32 Oct 26, 2022
Answering Open-Domain Questions of Varying Reasoning Steps from Text

This repository contains the authors' implementation of the Iterative Retriever, Reader, and Reranker (IRRR) model in the EMNLP 2021 paper "Answering Open-Domain Questions of Varying Reasoning Steps

26 Dec 22, 2022
Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2

Graph Transformer - Pytorch Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2. This was recently used by bot

Phil Wang 97 Dec 28, 2022