12 citations · 22 across the 10 of their papers we have counts for
3 papers · 1 filter
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations
Neha Kalibhat, Warren Morningstar, Alex Bijamov +3
Self-Supervised Learning (SSL) enables training performant models using limited labeled data. One of the pillars underlying vision SSL is the use of data augmentations/perturbation…
Adapting Self-Supervised Representations to Multi-Domain Setups
Neha Kalibhat, Sam Sharpe, Jeremy Goodsitt +2
Current state-of-the-art self-supervised approaches, are effective when trained on individual domains but show limited generalization on unseen domains. We observe that these model…
Identifying Interpretable Subspaces in Image Representations
Neha Kalibhat, Shweta Bhardwaj, Bayan Bruss +3
We propose Automatic Feature Explanation using Contrasting Concepts (FALCON), an interpretability framework to explain features of image representations. For a target feature, FALC…