  Self-supervised Learning (SSL) aims to learn transferable feature
representations for downstream applications without relying on labeled data.
The Barlow Twins algorithm, renowned for its widespread adoption and
straightforward implementation compared to its counterparts like contrastive
learning methods, minimizes feature redundancy while maximizing invariance to
common corruptions. Optimizing for the above objective forces the network to
learn useful representations, while avoiding noisy or constant features,
resulting in improved downstream task performance with limited adaptation.
Despite Barlow Twins' proven effectiveness in pre-training, the underlying SSL
objective can inadvertently cause feature overfitting due to the lack of strong
interaction between the samples unlike the contrastive learning approaches.
From our experiments, we observe that optimizing for the Barlow Twins objective
doesn't necessarily guarantee sustained improvements in representation quality
beyond a certain pre-training phase, and can potentially degrade downstream
performance on some datasets. To address this challenge, we introduce Mixed
Barlow Twins, which aims to improve sample interaction during Barlow Twins
training via linearly interpolated samples. This results in 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. The code
and checkpoints are available at: https://github.com/wgcban/mix-bt.git
