Computer Science > Machine Learning
arXiv:2311.09620 (cs)
[Submitted on 16 Nov 2023 (v1), last revised 16 Jan 2024 (this version, v2)]
Title:GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
View a PDF of the paper titled GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection, by Jinggang Chen and 5 other authors
View PDFAbstract:Detecting out-of-distribution (OOD) examples is crucial to guarantee the reliability and safety of deep neural networks in real-world settings. In this paper, we offer an innovative perspective on quantifying the disparities between in-distribution (ID) and OOD data -- analyzing the uncertainty that arises when models attempt to explain their predictive decisions. This perspective is motivated by our observation that gradient-based attribution methods encounter challenges in assigning feature importance to OOD data, thereby yielding divergent explanation patterns. Consequently, we investigate how attribution gradients lead to uncertain explanation outcomes and introduce two forms of abnormalities for OOD detection: the zero-deflation abnormality and the channel-wise average abnormality. We then propose GAIA, a simple and effective approach that incorporates Gradient Abnormality Inspection and Aggregation. The effectiveness of GAIA is validated on both commonly utilized (CIFAR) and large-scale (ImageNet-1k) benchmarks. Specifically, GAIA reduces the average FPR95 by 23.10% on CIFAR10 and by 45.41% on CIFAR100 compared to advanced post-hoc methods.
| Comments: | Accepted by NeurIPS2023 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2311.09620 [cs.LG] |
| (orarXiv:2311.09620v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2311.09620 arXiv-issued DOI via DataCite |
Submission history
From: Jinggang Chen [view email][v1] Thu, 16 Nov 2023 07:05:12 UTC (2,546 KB)
[v2] Tue, 16 Jan 2024 12:26:08 UTC (2,539 KB)
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View a PDF of the paper titled GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection, by Jinggang Chen and 5 other authors
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