Know Yourself Better: Diverse Discriminative Feature Learning Improves Open Set Recognition
This addresses the challenge of detecting novel classes during inference in machine learning, which is critical for real-world applications, but the approach appears incremental as it builds on existing heuristic methods.
The paper tackles the problem of open set recognition (OSR) by analyzing feature diversity, finding that learning diverse discriminative features improves performance, and proposes a novel method that achieves substantial improvement over state-of-the-art methods on a standard testbench.
Open set recognition (OSR) is a critical aspect of machine learning, addressing the challenge of detecting novel classes during inference. Within the realm of deep learning, neural classifiers trained on a closed set of data typically struggle to identify novel classes, leading to erroneous predictions. To address this issue, various heuristic methods have been proposed, allowing models to express uncertainty by stating "I don't know." However, a gap in the literature remains, as there has been limited exploration of the underlying mechanisms of these methods. In this paper, we conduct an analysis of open set recognition methods, focusing on the aspect of feature diversity. Our research reveals a significant correlation between learning diverse discriminative features and enhancing OSR performance. Building on this insight, we propose a novel OSR approach that leverages the advantages of feature diversity. The efficacy of our method is substantiated through rigorous evaluation on a standard OSR testbench, demonstrating a substantial improvement over state-of-the-art methods.