Semantically Coherent Out-of-Distribution Detection
- URL: http://arxiv.org/abs/2108.11941v1
- Date: Thu, 26 Aug 2021 17:53:32 GMT
- Title: Semantically Coherent Out-of-Distribution Detection
- Authors: Jingkang Yang, Haoqi Wang, Litong Feng, Xiaopeng Yan, Huabin Zheng,
Wayne Zhang, Ziwei Liu
- Abstract summary: Current out-of-distribution (OOD) detection benchmarks are commonly built by defining one dataset as in-distribution (ID) and all others as OOD.
We re-design the benchmarks and propose the semantically coherent out-of-distribution detection (SC-OOD)
Our approach achieves state-of-the-art performance on SC-OOD benchmarks.
- Score: 26.224146828317277
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Current out-of-distribution (OOD) detection benchmarks are commonly built by
defining one dataset as in-distribution (ID) and all others as OOD. However,
these benchmarks unfortunately introduce some unwanted and impractical goals,
e.g., to perfectly distinguish CIFAR dogs from ImageNet dogs, even though they
have the same semantics and negligible covariate shifts. These unrealistic
goals will result in an extremely narrow range of model capabilities, greatly
limiting their use in real applications. To overcome these drawbacks, we
re-design the benchmarks and propose the semantically coherent
out-of-distribution detection (SC-OOD). On the SC-OOD benchmarks, existing
methods suffer from large performance degradation, suggesting that they are
extremely sensitive to low-level discrepancy between data sources while
ignoring their inherent semantics. To develop an effective SC-OOD detection
approach, we leverage an external unlabeled set and design a concise framework
featured by unsupervised dual grouping (UDG) for the joint modeling of ID and
OOD data. The proposed UDG can not only enrich the semantic knowledge of the
model by exploiting unlabeled data in an unsupervised manner, but also
distinguish ID/OOD samples to enhance ID classification and OOD detection tasks
simultaneously. Extensive experiments demonstrate that our approach achieves
state-of-the-art performance on SC-OOD benchmarks. Code and benchmarks are
provided on our project page: https://jingkang50.github.io/projects/scood.
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