A modality-aware post-hoc detector for multi-modal OOD detection in action recognition combines uni-modal prediction relationships with feature-space scores and outperforms prior methods on the MultiOOD benchmark.
Why is the mahalanobis distance effective for anomaly detection?arXiv preprint arXiv:2003.00402
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
VOLTA, consisting of a deep encoder with learnable prototypes plus cross-entropy and post-hoc temperature scaling, matches or exceeds ten UQ baselines in accuracy, achieves lower expected calibration error, and performs well on out-of-distribution detection across CIFAR, SVHN, and corruption shifts.
A hybrid ES-DRL controller uses VAE latent Mahalanobis OOD detection to switch between RL and ES modes for time-varying nonlinear systems.
citing papers explorer
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Modality-Aware Out-of-Distribution Detection for Multi-Modal Action Recognition
A modality-aware post-hoc detector for multi-modal OOD detection in action recognition combines uni-modal prediction relationships with feature-space scores and outperforms prior methods on the MultiOOD benchmark.
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VOLTA: The Surprising Ineffectiveness of Auxiliary Losses for Calibrated Deep Learning
VOLTA, consisting of a deep encoder with learnable prototypes plus cross-entropy and post-hoc temperature scaling, matches or exceeds ten UQ baselines in accuracy, achieves lower expected calibration error, and performs well on out-of-distribution detection across CIFAR, SVHN, and corruption shifts.
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Mahalanobis-Guided Latent OOD Detection for Hybrid ES-DRL Control in Time-Varying Systems
A hybrid ES-DRL controller uses VAE latent Mahalanobis OOD detection to switch between RL and ES modes for time-varying nonlinear systems.