14 citations · 19 across the 4 of their papers we have counts for
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cs.LG2022★ 2 cited
TIER: Text-Image Entropy Regularization for CLIP-style models
Anil Palepu, Andrew L. Beam
In this paper, we introduce a novel regularization scheme on contrastive language-image pre-trained (CLIP) medical vision models. Our approach is based on the observation that on m…
cs.LG2022★ 3 cited
Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction
Bhawesh Kumar, Anil Palepu, Rudraksh Tuwani +1
Self-supervised models trained with a contrastive loss such as CLIP have shown to be very powerful in zero-shot classification settings. However, to be used as a zero-shot classifi…
cs.LG2022
Self-Supervision on Images and Text Reduces Reliance on Visual Shortcut Features
Anil Palepu, Andrew L Beam
Deep learning models trained in a fully supervised manner have been shown to rely on so-called "shortcut" features. Shortcut features are inputs that are associated with the outcom…