Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement

• URL: http://arxiv.org/abs/2310.00227v1
• Date: Sat, 30 Sep 2023 02:10:54 GMT
• Title: Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement
• Authors: Kai Xu, Rongyu Chen, Gianni Franchi, Angela Yao
• Abstract summary: In this paper, we offer insights and analyses of recent state-of-the-art out-of-distribution (OOD) detection methods. We demonstrate that activation pruning has a detrimental effect on OOD detection, while activation scaling enhances it. We achieve AUROC scores of +1.85% for near-OOD and +0.74% for far-OOD datasets on the OpenOOD v1.5 ImageNet-1K benchmark.
• Score: 41.650761556671775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The capacity of a modern deep learning system to determine if a sample falls within its realm of knowledge is fundamental and important. In this paper, we offer insights and analyses of recent state-of-the-art out-of-distribution (OOD) detection methods - extremely simple activation shaping (ASH). We demonstrate that activation pruning has a detrimental effect on OOD detection, while activation scaling enhances it. Moreover, we propose SCALE, a simple yet effective post-hoc network enhancement method for OOD detection, which attains state-of-the-art OOD detection performance without compromising in-distribution (ID) accuracy. By integrating scaling concepts into the training process to capture a sample's ID characteristics, we propose Intermediate Tensor SHaping (ISH), a lightweight method for training time OOD detection enhancement. We achieve AUROC scores of +1.85\% for near-OOD and +0.74\% for far-OOD datasets on the OpenOOD v1.5 ImageNet-1K benchmark. Our code and models are available at https://github.com/kai422/SCALE.

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