Typicalness-Aware Learning for Failure Detection

Yijun Liu

Abstract:

Deep neural networks (DNNs) often suffer from the overconfidence issue, where
incorrect predictions are made with high confidence scores, hindering the appli3 cations in critical systems. In this paper, we propose a novel approach called
Typicalness-Aware Learning (TAL) to address this issue and improve failure detec5 tion performance. We observe that, with the cross-entropy loss, model predictions
are optimized to align with the corresponding labels via increasing logit magnitude or refining logit direction. However, regarding atypical samples, the image content
and their labels may exhibit disparities. This discrepancy can lead to overfitting
on atypical samples, ultimately resulting in the overconfidence issue that we aim
to address. To tackle the problem, we have devised a metric that quantifies the
typicalness of each sample, enabling the dynamic adjustment of the logit magnitude
during the training process. By allowing atypical samples to be adequately fitted
while preserving reliable logit direction, the problem of overconfidence can be mitigated. TAL has been extensively evaluated on benchmark datasets, and the results
demonstrate its superiority over existing failure detection methods. Specifically,
TAL achieves a more than 5% improvement on CIFAR100 in terms of the Area
Under the Risk-Coverage Curve (AURC) compared to the state-of-the-art.