A framework models DNN layer weight-activation interactions via Bernoulli distributions and uses class separation as a diagnostic proxy to quantify distributional robustness, tested on CIFAR-10 and ImageNet models.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
High accuracy in noisy-label learning does not guarantee OOD detection reliability due to uncertainty collapse, and Virtual Margin Regularization offers partial mitigation.
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A New Framework to Analyse the Distributional Robustness of Deep Neural Networks
A framework models DNN layer weight-activation interactions via Bernoulli distributions and uses class separation as a diagnostic proxy to quantify distributional robustness, tested on CIFAR-10 and ImageNet models.
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When Accuracy Is Not Enough: Uncertainty Collapse between Noisy Label Learning and Out-of-Distribution Detection
High accuracy in noisy-label learning does not guarantee OOD detection reliability due to uncertainty collapse, and Virtual Margin Regularization offers partial mitigation.