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Towards Few-shot Out-of-Distribution Detection (2311.12076v3)

Published 20 Nov 2023 in cs.CV

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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Authors (7)
  1. Jiuqing Dong (1 paper)
  2. Yongbin Gao (6 papers)
  3. Heng Zhou (47 papers)
  4. Jun Cen (28 papers)
  5. Yifan Yao (11 papers)
  6. Sook Yoon (7 papers)
  7. Park Dong Sun (1 paper)
Citations (3)
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