Towards Few-shot Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2311.12076v3
• Date: Wed, 31 Jan 2024 02:01:11 GMT
• Title: Towards Few-shot Out-of-Distribution Detection
• Authors: Jiuqing Dong, Yongbin Gao, Heng Zhou, Jun Cen, Yifan Yao, Sook Yoon, Park Dong Sun
• Abstract summary: Out-of-distribution (OOD) detection is critical for ensuring the reliability of open-world intelligent systems. Our study identifies a significant performance drop under the scarcity of training samples. We introduce a novel few-shot OOD detection benchmark, carefully constructed to address this gap.
• Score: 6.297489935041838
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-distribution (OOD) detection is critical for ensuring the reliability of open-world intelligent systems. Despite the notable advancements in existing OOD detection methodologies, our study identifies a significant performance drop under the scarcity of training samples. In this context, we introduce a novel few-shot OOD detection benchmark, carefully constructed to address this gap. Our empirical analysis reveals the superiority of ParameterEfficient Fine-Tuning (PEFT) strategies, such as visual prompt tuning and visual adapter tuning, over conventional techniques, including fully fine-tuning and linear probing tuning in the few-shot OOD detection task. Recognizing some crucial information from the pre-trained model, which is pivotal for OOD detection, may be lost during the fine-tuning process, we propose a method termed DomainSpecific and General Knowledge Fusion (DSGF). This approach is designed to be compatible with diverse fine-tuning frameworks. Our experiments show that the integration of DSGF significantly enhances the few-shot OOD detection capabilities across various methods and fine-tuning methodologies, including fully fine-tuning, visual adapter tuning, and visual prompt tuning. The code will be released.

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