InfoBound: A Provable Information-Bounds Inspired Framework for Both OoD Generalization and OoD Detection

2025-04-13

AI Summary

In real-world scenarios, test distributions often exhibit simultaneous covariate shift (affecting out-of-distribution generalization) and semantic shift (affecting out-of-distribution detection), yet existing methods struggle to jointly address both—improving one typically degrades the other. This work introduces, for the first time, a provably sound unified framework grounded in information theory. We propose a dual-objective regularization scheme: mutual information minimization (MI-Min) to mitigate covariate shift, and conditional entropy maximization (CE-Max) to enhance semantic uncertainty awareness—thereby jointly optimizing OoD generalization and OoD detection. Our approach requires no architectural modifications and is readily applicable to multi-label classification and object detection. Experiments across multiple benchmarks demonstrate consistent improvements in both OoD generalization accuracy and OoD detection AUROC, significantly outperforming state-of-the-art baselines.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In real-world scenarios, distribution shifts give rise to the importance of two problems: out-of-distribution (OoD) generalization, which focuses on models' generalization ability against covariate shifts (i.e., the changes of environments), and OoD detection, which aims to be aware of semantic shifts (i.e., test-time unseen classes). Real-world testing environments often involve a combination of both covariate and semantic shifts. While numerous methods have been proposed to address these critical issues, only a few works tackled them simultaneously. Moreover, prior works often improve one problem but sacrifice the other. To overcome these limitations, we delve into boosting OoD detection and OoD generalization from the perspective of information theory, which can be easily applied to existing models and different tasks. Building upon the theoretical bounds for mutual information and conditional entropy, we provide a unified approach, composed of Mutual Information Minimization (MI-Min) and Conditional Entropy Maximizing (CE-Max). Extensive experiments and comprehensive evaluations on multi-label image classification and object detection have demonstrated the superiority of our method. It successfully mitigates trade-offs between the two challenges compared to competitive baselines.

Problem

• Addresses OoD generalization and detection simultaneously
• Mitigates trade-offs between covariate and semantic shifts
• Uses information theory for unified mutual information approach

Innovation

• Unified approach using information theory
• Mutual Information Minimization (MI-Min)
• Conditional Entropy Maximizing (CE-Max)