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}
}

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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

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