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arXiv · 2210.15805

Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction

Bhawesh Kumar·Anil Palepu·Rudraksh Tuwani·Andrew Beam
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Abstract

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 classifier these models require the user to provide new captions over a fixed set of labels at test time. In many settings, it is hard or impossible to know if a new query caption is compatible with the source captions used to train the model. We address these limitations by framing the zero-shot classification task as an outlier detection problem and develop a conformal prediction procedure to assess when a given test caption may be reliably used. On a real-world medical example, we show that our proposed conformal procedure improves the reliability of CLIP-style models in the zero-shot classification setting, and we provide an empirical analysis of the factors that may affect its performance.

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Bhawesh Kumar, Anil Palepu, Rudraksh Tuwani, Andrew Beam. 2022-10-27. Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction. https://arxiv.org/abs/2210.15805

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