MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning

• URL: http://arxiv.org/abs/2411.02444v1
• Date: Sat, 02 Nov 2024 17:46:23 GMT
• Title: MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning
• Authors: Haoliang Wang, Chen Zhao, Feng Chen,
• Abstract summary: We introduce Meta-learned Across Domain Out-of-distribution Detection (MADOD), a novel framework designed to address both shifts concurrently. Our key innovation lies in task construction: we randomly designate in-distribution classes as pseudo-OODs within each meta-learning task. Experiments on real-world and synthetic datasets demonstrate MADOD's superior performance in semantic OOD detection across unseen domains.
• Score: 10.38552112657656
• License:
• Abstract: Real-world machine learning applications often face simultaneous covariate and semantic shifts, challenging traditional domain generalization and out-of-distribution (OOD) detection methods. We introduce Meta-learned Across Domain Out-of-distribution Detection (MADOD), a novel framework designed to address both shifts concurrently. MADOD leverages meta-learning and G-invariance to enhance model generalizability and OOD detection in unseen domains. Our key innovation lies in task construction: we randomly designate in-distribution classes as pseudo-OODs within each meta-learning task, simulating OOD scenarios using existing data. This approach, combined with energy-based regularization, enables the learning of robust, domain-invariant features while calibrating decision boundaries for effective OOD detection. Operating in a test domain-agnostic setting, MADOD eliminates the need for adaptation during inference, making it suitable for scenarios where test data is unavailable. Extensive experiments on real-world and synthetic datasets demonstrate MADOD's superior performance in semantic OOD detection across unseen domains, achieving an AUPR improvement of 8.48% to 20.81%, while maintaining competitive in-distribution classification accuracy, representing a significant advancement in handling both covariate and semantic shifts.

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