  Self-supervised representation learning (SSL) methods provide an effective
label-free initial condition for fine-tuning downstream tasks. However, in
numerous realistic scenarios, the downstream task might be biased with respect
to the target label distribution. This in turn moves the learned fine-tuned
model posterior away from the initial (label) bias-free self-supervised model
posterior. In this work, we re-interpret SSL fine-tuning under the lens of
Bayesian continual learning and consider regularization through the Elastic
Weight Consolidation (EWC) framework. We demonstrate that self-regularization
against an initial SSL backbone improves worst sub-group performance in
Waterbirds by 5% and Celeb-A by 2% when using the ViT-B/16 architecture.
Furthermore, to help simplify the use of EWC with SSL, we pre-compute and
publicly release the Fisher Information Matrix (FIM), evaluated with 10,000
ImageNet-1K variates evaluated on large modern SSL architectures including
ViT-B/16 and ResNet50 trained with DINO.
