Open-Set Domain Adaptation Under Background Distribution Shift: Challenges and A Provably Efficient Solution

2025-11-30

AI Summary

In real-world scenarios, open-set recognition confronts a dual challenge: the emergence of novel classes alongside dynamic background distribution shifts of known classes. Existing methods typically assume a stationary background distribution and thus lack robustness to this joint shift. This paper proposes the first theoretically grounded open-set recognition framework capable of handling background distribution shifts. Grounded in a separability assumption, our method designs a robust algorithm and establishes performance bounds under over-parameterized settings, explicitly characterizing how novel-class cardinality and distribution shift jointly affect recognition capability. We further introduce a scalable, noise-resilient implementation strategy. Extensive experiments across multiple image and text benchmarks demonstrate significant improvements over state-of-the-art methods, validating both effectiveness and generalization superiority under dynamic distributions.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

As we deploy machine learning systems in the real world, a core challenge is to maintain a model that is performant even as the data shifts. Such shifts can take many forms: new classes may emerge that were absent during training, a problem known as open-set recognition, and the distribution of known categories may change. Guarantees on open-set recognition are mostly derived under the assumption that the distribution of known classes, which we call emph{the background distribution}, is fixed. In this paper we develop ours{}, a method that is guaranteed to solve open-set recognition even in the challenging case where the background distribution shifts. We prove that the method works under benign assumptions that the novel class is separable from the non-novel classes, and provide theoretical guarantees that it outperforms a representative baseline in a simplified overparameterized setting. We develop techniques to make ours{} scalable and robust, and perform comprehensive empirical evaluations on image and text data. The results show that ours{} significantly outperforms existing open-set recognition methods under background shift. Moreover, we provide new insights into how factors such as the size of the novel class influences performance, an aspect that has not been extensively explored in prior work.

Problem

• Addresses open-set recognition with shifting background distribution.
• Ensures novel class separation from known classes theoretically.
• Outperforms existing methods under background shift empirically.

Innovation

• Method for open-set recognition under background shift
• Scalable and robust techniques for image and text data
• Theoretical guarantees outperforming baseline in overparameterized setting