Dynamic Against Dynamic: An Open-set Self-learning Framework

TL;DR

This paper introduces a dynamic open-set recognition framework that adapts during testing by utilizing unknown samples to improve model performance in changing environments.

Contribution

It proposes a novel self-learning framework with a self-matching module that dynamically adapts and leverages unknown samples to enhance recognition accuracy.

Findings

Achieves new state-of-the-art results on multiple benchmarks.

Effectively utilizes unknown samples for model adaptation.

Demonstrates robustness in dynamic open-set scenarios.

Abstract

In open-set recognition, existing methods generally learn statically fixed decision boundaries using known classes to reject unknown classes. Though they have achieved promising results, such decision boundaries are evidently insufficient for universal unknown classes in dynamic and open scenarios as they can potentially appear at any position in the feature space. Moreover, these methods just simply reject unknown class samples during testing without any effective utilization for them. In fact, such samples completely can constitute the true instantiated representation of the unknown classes to further enhance the model's performance. To address these issues, this paper proposes a novel dynamic against dynamic idea, i.e., dynamic method against dynamic changing open-set world, where an open-set self-learning (OSSL) framework is correspondingly developed. OSSL starts with a good closed-set classifier trained by known classes and utilizes available test samples for model adaptation during testing, thus gaining the adaptability to changing data distributions. In particular, a novel self-matching module is designed for OSSL, which can achieve the adaptation in automatically identifying known class samples while rejecting unknown class samples which are further utilized to enhance the discriminability of the model as the instantiated representation of unknown classes. Our method establishes new performance milestones respectively in almost all standard and cross-data benchmarks.