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

LONG-TERM SERIES FORECASTING WITH QUERYSELECTOR – EFFICIENT MODEL OF SPARSEATTENTION

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

A curated list of Generative Deep Art projects, tools, artworks, and models

[ICSE2020] MemLock: Memory Usage Guided Fuzzing

PyTorch implementation of the Deep SLDA method from our CVPRW-2020 paper "Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis"

banditml is a lightweight contextual bandit & reinforcement learning library designed to be used in production Python services.

nnFormer: Interleaved Transformer for Volumetric Segmentation

Compact Bidirectional Transformer for Image Captioning

Implementation of the state of the art beat-detection, downbeat-detection and tempo-estimation model

OpenMMLab Computer Vision Foundation

Utilizes Pose Estimation to offer sprinters cues based on an image of their running form.

MiniSom is a minimalistic implementation of the Self Organizing Maps

TSIT: A Simple and Versatile Framework for Image-to-Image Translation

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

TensorFlow-based implementation of "ICNet for Real-Time Semantic Segmentation on High-Resolution Images".

A tool to analyze leveraged liquidity mining and find optimal option combination for hedging.

Pytorch based library to rank predicted bounding boxes using text/image user's prompts.

Solving SMPL/MANO parameters from keypoint coordinates.

An Active Automata Learning Library Written in Python

Code for Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing(ICCV21)