GAE reduces the faithfulness gap in dictionary-based explainers under distribution shift by geometrically realigning the ID dictionary to the OOD-active subspace, with a quadratic excess-loss bound.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
CNN feature activations follow long-tailed Weibull-like distributions with increasing tail dependence by depth rather than Gaussian, indicating a Matthew process that concentrates signal in tails.
ConjNorm reframes OOD detection score design as optimizing norm p in an exponential family density model via a Bregman divergence theorem, with a tractable Monte Carlo estimator, claiming SOTA gains on CIFAR-100 and ImageNet-1K.
citing papers explorer
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Geometry-Adaptive Explainer for Faithful Dictionary-Based Interpretability under Distribution Shift
GAE reduces the faithfulness gap in dictionary-based explainers under distribution shift by geometrically realigning the ID dictionary to the OOD-active subspace, with a quadratic excess-loss bound.
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Why CNN Features Are not Gaussian: A Statistical Anatomy of Deep Representations
CNN feature activations follow long-tailed Weibull-like distributions with increasing tail dependence by depth rather than Gaussian, indicating a Matthew process that concentrates signal in tails.
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ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection
ConjNorm reframes OOD detection score design as optimizing norm p in an exponential family density model via a Bregman divergence theorem, with a tractable Monte Carlo estimator, claiming SOTA gains on CIFAR-100 and ImageNet-1K.