CEDAR learns an invertible rotation of vision-language embeddings to concentrate semantics into sparse, axis-aligned coordinates for improved interpretability.
Infodisent: Explainability of image classification models by information disentanglement
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ProDG generates high-fidelity prototypes from model weights alone for data-free post-hoc explainability in prototype-based networks.
APEX generates four types of prototype-based explanations for pre-trained audio classifiers that preserve output invariance and target acoustic properties better than gradient methods applied to spectrograms.
citing papers explorer
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Conceptualizing Embeddings: Sparse Disentanglement for Vision-Language Models
CEDAR learns an invertible rotation of vision-language embeddings to concentrate semantics into sparse, axis-aligned coordinates for improved interpretability.
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ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability
ProDG generates high-fidelity prototypes from model weights alone for data-free post-hoc explainability in prototype-based networks.
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APEX: Audio Prototype EXplanations for Classification Tasks
APEX generates four types of prototype-based explanations for pre-trained audio classifiers that preserve output invariance and target acoustic properties better than gradient methods applied to spectrograms.