Implementation for paper "Towards the Generalization of Contrastive Self-Supervised Learning"

Last update: Nov 30, 2022

Overview

Contrastive Self-Supervised Learning on CIFAR-10

Paper

"Towards the Generalization of Contrastive Self-Supervised Learning", Weiran Huang, Mingyang Yi and Xuyang Zhao, arXiv:2111.00743, 2021.

The most critical argument we made in our paper is that the quality of data augmentation exhibits great impact on the quality of contrastive-learned encoder. The data augmentation with sharper intra-class concentration enables the model to have better generalization on downstream tasks. We verify it through a variety of experiments in this repository.

Supported methods

SimCLR
Barlow Twins
MoCo
SimSiam

Installation

pip install -r requirement.txt

Dependencies

torch==1.4.0
torchvision==0.5.0
pytorch-lightning==1.3.8
lightly==1.0.8 (important!)

Evaluation

KNN evaluation protocol. Code from here.

Results

ResNet-18 trained by SimCLR and Barlow Twins over various data augmentation combinations.

Example: python main.py --model=twins --epoch=800 --batch=512 --round=3 --augs=abcde

(a)	(b)	(c)	(d)	(e)	SimCLR	Barlow Twins
✓	✓	✓	✓	✓	89.92 ± 0.05	83.93 ± 0.57
✓	✓	✓	✓	×	88.41 ± 0.11	83.37 ± 0.43
✓	✓	✓	×	×	83.62 ± 0.19	73.70 ± 0.99
✓	✓	×	×	×	62.91 ± 0.25	49.56 ± 0.11
✓	×	×	×	×	62.37 ± 0.09	48.54 ± 0.29

Augmentation operations include:

(a) random cropping with a scaling factor chosen in [0.08, 1.0];

(b) random Gaussian blur with a probability 0.5;

(d) color distortion with a probability of 0.8 and with strength of [0.4, 0.4, 0.4, 0.1];

(e) random horizontal flipping with a probability of 0.5.

ResNet18 trained by SimCLR and Barlow Twins over various color distortion strengths.

Example: python main.py --model=simclr --epoch=800 --batch=512 --round=3 --augs=color --strength=1

Color Distortion Strength	SimCLR	Barlow Twins
1/8	73.60 ± 0.11	61.13 ± 2.81
1/4	76.25 ± 0.16	68.30 ± 0.15
1/2	78.49 ± 0.09	72.76 ± 1.50
1	82.64 ± 0.57	78.79 ± 0.54

Acknowledgement

This code is based on:

Citation

If you find our work useful in your research, please consider citing:

@misc{huang2021generalization,
      title={Towards the Generalization of Contrastive Self-Supervised Learning}, 
      author={Weiran Huang and Mingyang Yi and Xuyang Zhao},
      year={2021},
      eprint={2111.00743},
      archivePrefix={arXiv}
}

You might also like...

Implementation of Nyström Self-attention, from the paper Nyströmformer

Nyström Attention Implementation of Nyström Self-attention, from the paper Nyströmformer. Yannic Kilcher video Install $ pip install nystrom-attention

95 Jan 2, 2023

Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

SETR - Pytorch Since the original paper (Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.) has no official

112 Dec 16, 2022

Official implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis https://arxiv.org/abs/2011.13775

CIPS -- Official Pytorch Implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis Requirements pip install -r requi

Multimodal Lab @ Samsung AI Center Moscow

201 Dec 21, 2022

Official pytorch implementation of paper

Implementation for paper "Towards the Generalization of Contrastive Self-Supervised Learning"

Related tags

Overview

Contrastive Self-Supervised Learning on CIFAR-10

Paper

Supported methods

Installation

Dependencies

Evaluation

Results

ResNet-18 trained by SimCLR and Barlow Twins over various data augmentation combinations.

ResNet18 trained by SimCLR and Barlow Twins over various color distortion strengths.

Acknowledgement

Citation

You might also like...

Implementation of Nyström Self-attention, from the paper Nyströmformer

Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

Official implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis https://arxiv.org/abs/2011.13775

Official pytorch implementation of paper "Image-to-image Translation via Hierarchical Style Disentanglement".

PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 2021

Implementation of Barlow Twins paper

Official pytorch implementation of paper "Inception Convolution with Efficient Dilation Search" (CVPR 2021 Oral).

Official implementation of our paper "LLA: Loss-aware Label Assignment for Dense Pedestrian Detection" in Pytorch.

Functional TensorFlow Implementation of Singular Value Decomposition for paper Fast Graph Learning

Releases(v0.2.1)

v0.2.1(Jun 16, 2022)

v0.2.0(Jun 4, 2022)

v0.1.0(Jun 4, 2022)

Owner

Weiran Huang

SpiroMask: Measuring Lung Function Using Consumer-Grade Masks

Immortal tracker

Code for DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning

A script that trains a model to recognize handwritten digits using the MNIST data set.

Merlion: A Machine Learning Framework for Time Series Intelligence

Code for paper Adaptively Aligned Image Captioning via Adaptive Attention Time

Official implementation for "Style Transformer for Image Inversion and Editing" (CVPR 2022)

Intrinsic Image Harmonization

AquaTimer - Programmable Timer for Aquariums based on ATtiny414/814/1614

Source code of AAAI 2022 paper "Towards End-to-End Image Compression and Analysis with Transformers".

✨风纪委员会自动投票脚本，利用Github Action帮你进行裁决操作（为了让其他风纪委员有案件可判，本程序从中午12点才开始运行，有需要请自己修改运行时间）

CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022)

MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images

3DIAS: 3D Shape Reconstruction with Implicit Algebraic Surfaces (ICCV 2021)

ZeroGen: Efficient Zero-shot Learning via Dataset Generation

MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images (ISBI 2021, MELBA 2021)

On the model-based stochastic value gradient for continuous reinforcement learning

Deep Implicit Moving Least-Squares Functions for 3D Reconstruction

Snapchat-filters-app-opencv-python - Here we used opencv and other inbuilt python modules to create filter application like snapchat

Discovering Dynamic Salient Regions with Spatio-Temporal Graph Neural Networks