HiProto uses hierarchical prototypes with RPC-Loss, PR-Loss, and SPLGS to deliver competitive, interpretable object detection on low-quality datasets like ExDark and RTTS.
Deformable protopnet: An in- terpretable image classifier using deformable prototypes
2 Pith papers cite this work. Polarity classification is still indexing.
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PhiNet adds phonetic interpretability to speaker verification while matching the accuracy of standard black-box models on VoxCeleb, SITW, and LibriSpeech.
citing papers explorer
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HiProto: Hierarchical Prototype Learning for Interpretable Object Detection Under Low-quality Conditions
HiProto uses hierarchical prototypes with RPC-Loss, PR-Loss, and SPLGS to deliver competitive, interpretable object detection on low-quality datasets like ExDark and RTTS.
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PhiNet: Speaker Verification with Phonetic Interpretability
PhiNet adds phonetic interpretability to speaker verification while matching the accuracy of standard black-box models on VoxCeleb, SITW, and LibriSpeech.