Mixed Barlow Twins for Self-Supervised Representation Learning

Guarding Barlow Twins Against Overfitting with Mixed Samples

Wele Gedara Chaminda Bandara (Johns Hopkins University), Celso M. De Melo (U.S. Army Research Laboratory), and Vishal M. Patel (Johns Hopkins University)

1 Overview of Mixed Barlow Twins

TL;DR

Mixed Barlow Twins aims to improve sample interaction during Barlow Twins training via linearly interpolated samples.
We introduce an additional regularization term to the original Barlow Twins objective, assuming linear interpolation in the input space translates to linearly interpolated features in the feature space.
Pre-training with this regularization effectively mitigates feature overfitting and further enhances the downstream performance on CIFAR-10, CIFAR-100, TinyImageNet, STL-10, and ImageNet datasets.

$C^{MA} = (Z^M)^TZ^A$

$C^{MB} = (Z^M)^TZ^B$

$C^{MA}_{gt} = \lambda (Z^A)^TZ^A + (1-\lambda)\mathtt{Shuffle}^*(Z^B)^TZ^A$

$C^{MB}_{gt} = \lambda (Z^A)^TZ^B + (1-\lambda)\mathtt{Shuffle}^*(Z^B)^TZ^B$

2 Usage

2.1 Requirements

Before using this repository, make sure you have the following prerequisites installed:

You can install PyTorch with the following command (in Linux OS):

conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia

2.2 Installation

To get started, clone this repository:

git clone https://github.com/wgcban/mix-bt.git

Next, create the conda environment named ssl-aug by executing the following command:

conda env create -f environment.yml

All the train-val-test statistics will be automatically upload to wandb, and please refer wandb-quick-start documentation if you are not familiar with using wandb.

2.3 Supported Pre-training Datasets

This repository supports the following pre-training datasets:

CIFAR-10: https://www.cs.toronto.edu/~kriz/cifar.html
CIFAR-100: https://www.cs.toronto.edu/~kriz/cifar.html
Tiny-ImageNet: https://github.com/rmccorm4/Tiny-Imagenet-200
STL-10: https://cs.stanford.edu/~acoates/stl10/
ImageNet: https://www.image-net.org

CIFAR-10, CIFAR-100, and STL-10 datasets are directly available in PyTorch.

To use TinyImageNet, please follow the preprocessing instructions provided in the TinyImageNet-Script. Download these datasets and place them in the data directory.

2.4 Supported Transfer Learning Datasets

You can download and place transfer learning datasets under their respective paths, such as 'data/DTD'. The supported transfer learning datasets include:

DTD: https://www.robots.ox.ac.uk/~vgg/data/dtd/
MNIST: http://yann.lecun.com/exdb/mnist/
FashionMNIST: https://github.com/zalandoresearch/fashion-mnist
CUBirds: http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
VGGFlower: https://www.robots.ox.ac.uk/~vgg/data/flowers/102/
Traffic Signs: https://benchmark.ini.rub.de/gtsdb_dataset.html
Aircraft: https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/

2.5 Supported SSL Methods

This repository supports the following Self-Supervised Learning (SSL) methods:

SimCLR: contrastive learning for SSL
BYOL: distilation for SSL
Witening MSE: infomax for SSL
Barlow Twins: infomax for SSL
Mixed Barlow Twins (ours): infomax + mixed samples for SSL

2.6 Pre-Training with Mixed Barlow Twins

To start pre-training and obtain k-NN evaluation results for Mixed Barlow Twins on CIFAR-10, CIFAR-100, TinyImageNet, and STL-10 with ResNet-18/50 backbones, please run:

sh scripts-pretrain-resnet18/[dataset].sh

sh scripts-pretrain-resnet50/[dataset].sh

To start the pre-training on ImageNet with ResNet-50 backbone, please run:

sh scripts-pretrain-resnet18/imagenet.sh

2.7 Linear Evaluation of Pre-trained Models

Before running linear evaluation, ensure that you specify the model_path argument correctly in the corresponding .sh file.

To obtain linear evaluation results on CIFAR-10, CIFAR-100, TinyImageNet, STL-10 with ResNet-18/50 backbones, please run:

sh scripts-linear-resnet18/[dataset].sh

sh scripts-linear-resnet50/[dataset].sh

To obtain linear evaluation results on ImageNet with ResNet-50 backbone, please run:

sh scripts-linear-resnet50/imagenet_sup.sh

2.8 Transfer Learning of Pre-trained Models

To perform transfer learning from pre-trained models on CIFAR-10, CIFAR-100, and STL-10 to fine-grained classification datasets, execute the following command, making sure to specify the model_path argument correctly:

sh scripts-transfer-resnet18/[dataset]-to-x.sh

3 Pre-Trained Checkpoints

Download the pre-trained models from GitHub (Releases v1.0.0) and store them in checkpoints/. This repository provides pre-trained checkpoints for both ResNet-18 and ResNet-50 architectures.

3.1 ResNet-18

Dataset	$d$	$\lambda_{BT}$	$\lambda_{reg}$	Download Link to Pretrained Model	KNN Acc.	Linear Acc.
`CIFAR-10`	1024	0.0078125	4.0	4wdhbpcf_0.0078125_1024_256_cifar10_model.pth	90.52	92.58
`CIFAR-100`	1024	0.0078125	4.0	76kk7scz_0.0078125_1024_256_cifar100_model.pth	61.25	69.31
`TinyImageNet`	1024	0.0009765	4.0	02azq6fs_0.0009765_1024_256_tiny_imagenet_model.pth	38.11	51.67
`STL-10`	1024	0.0078125	2.0	i7det4xq_0.0078125_1024_256_stl10_model.pth	88.94	91.02

3.2 ResNet-50

Dataset	$d$	$\lambda_{BT}$	$\lambda_{reg}$	Download Link to Pretrained Model	KNN Acc.	Linear Acc.
`CIFAR-10`	1024	0.0078125	4.0	v3gwgusq_0.0078125_1024_256_cifar10_model.pth	91.39	93.89
`CIFAR-100`	1024	0.0078125	4.0	z6ngefw7_0.0078125_1024_256_cifar100_model.pth	64.32	72.51
`TinyImageNet`	1024	0.0009765	4.0	kxlkigsv_0.0009765_1024_256_tiny_imagenet_model.pth	42.21	51.84
`STL-10`	1024	0.0078125	2.0	pbknx38b_0.0078125_1024_256_stl10_model.pth	87.79	91.70

On ImageNet

epochs = 300

$d$ = 8192

$\lambda_{BT}$ = 0.0051

$\lambda_{reg}$	Download Link to Pretrained Model	Train Log	Download Link to Linear-Probed Model	Val. Log	Linear Acc.
0.0 (BT)	3on0l4wl_0.0000_8192_1024_imagenet_resnet50.pth	train_log	checkpoint_3tb4tcvp.pth	val_log	71.3
0.0025	l418b9zw_0.0025_8192_1024_imagenet_resnet50.pth	train_log	checkpoint_09g7ytcz.pth	val_log	70.9
0.1	13awtq23_0.1000_8192_1024_imagenet_resnet50.pth	train_log	checkpoint_pgawzr4e.pth	val_log	71.6
1.0	3fb1op86_1.0000_8192_1024_imagenet_resnet50.pth	train_log	checkpoint_wvi0hle8.pth	val_log	72.2
2.0	5n9yqio0_1.0000_8192_1024_imagenet_resnet50.pth	train_log	checkpoint_09g7ytcz.pth	val_log	xx.x
3.0	q03u2xjz_3.0000_8192_1024_imagenet_resnet50.pth	train_log	checkpoint_00atvp6x.pth	val_log	72.0
5.0	TBU	TBU

4 Training Statistics

Here we provide some training and validation (linear probing) statistics for Barlow Twins vs. Mixed Barlow Twins with ResNet-50 backbone on ImageNet:

5 Disclaimer

A large portion of the code is from Barlow Twins HSIC (for experiments on small datasets: CIFAR-10, CIFAR-100, TinyImageNet, and STL-10) and official implementation of Barlow Twins here (for experiments on ImageNet), which is a great resource for academic development.

Also, note that the implementation of SOTA methods (SimCLR, BYOL, and Witening-MSE) in ssl-sota are copied from Witening-MSE.

We would like to thank all of them for making their repositories publicly available for the research community. 🙏

6 Reference

If you feel our work is useful, please consider citing our work. Thanks!

@misc{bandara2023guarding,
      title={Guarding Barlow Twins Against Overfitting with Mixed Samples}, 
      author={Wele Gedara Chaminda Bandara and Celso M. De Melo and Vishal M. Patel},
      year={2023},
      eprint={2312.02151},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

7 License

This code is under MIT licence, you can find the complete file here.

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