Detecting Out-of-distribution Examples via Class-conditional Impressions Reappearing

• URL: http://arxiv.org/abs/2303.09746v1
• Date: Fri, 17 Mar 2023 02:55:08 GMT
• Title: Detecting Out-of-distribution Examples via Class-conditional Impressions Reappearing
• Authors: Jinggang Chen, Xiaoyang Qu, Junjie Li, Jianzong Wang, Jiguang Wan, Jing Xiao
• Abstract summary: Out-of-distribution (OOD) detection aims at enhancing standard deep neural networks to distinguish anomalous inputs from original training data. Due to privacy and security, auxiliary data tends to be impractical in a real-world scenario. We propose a data-free method without training on natural data, called Class-Conditional Impressions Reappearing (C2IR)
• Score: 30.938412222724608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Out-of-distribution (OOD) detection aims at enhancing standard deep neural networks to distinguish anomalous inputs from original training data. Previous progress has introduced various approaches where the in-distribution training data and even several OOD examples are prerequisites. However, due to privacy and security, auxiliary data tends to be impractical in a real-world scenario. In this paper, we propose a data-free method without training on natural data, called Class-Conditional Impressions Reappearing (C2IR), which utilizes image impressions from the fixed model to recover class-conditional feature statistics. Based on that, we introduce Integral Probability Metrics to estimate layer-wise class-conditional deviations and obtain layer weights by Measuring Gradient-based Importance (MGI). The experiments verify the effectiveness of our method and indicate that C2IR outperforms other post-hoc methods and reaches comparable performance to the full access (ID and OOD) detection method, especially in the far-OOD dataset (SVHN).

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