Balanced Hyperbolic Embeddings Are Natural Out-of-Distribution Detectors
Keywords: Hyperbolic learning, Out-of-distribution detection
Abstract: Out-of-distribution recognition forms an important and well-studied problem in computer vision, with the goal to filter out samples that do not belong to the distribution on which a network has been trained. The conclusion of this paper is simple: a good hierarchical hyperbolic embedding is preferred for discriminating in- and out-of-distribution samples. We introduce Balanced Hyperbolic Learning. We outline a hyperbolic class embedding algorithm that jointly optimizes for hierarchical distortion and balancing between shallow and wide subhierarchies. We can then use the class embeddings as hyperbolic prototypes for classification on in-distribution data. We outline how existing out-of-distribution scoring functions can be generalized to operate with hyperbolic prototypes. Empirical evaluations across 13 datasets and 13 scoring functions show that our hyperbolic embeddings outperform existing out-of-distribution approaches when trained on the same data with the same backbones. We also show that our hyperbolic embeddings outperform other hyperbolic approaches and naturally enable hierarchical out-of-distribution generalization.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 7135
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