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.
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, 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:
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 checkpoints/ and store them in checkpoints/. This repository provides pre-trained checkpoints for both ResNet-18 and ResNet-50 architectures.
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.
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!
bibtex
1@misc{bandara2023guarding,
2 title={Guarding Barlow Twins Against Overfitting with Mixed Samples},
3 author={Wele Gedara Chaminda Bandara and Celso M. De Melo and Vishal M. Patel},
4 year={2023},
5 eprint={2312.02151},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV}
8}
7 License
This code is under MIT licence, you can find the complete file here.
