Empirical Evidences for the Effects of Feature Diversity in Open Set Recognition and Continual Learning

2025-08-18

AI Summary

This work investigates the critical role of feature diversity in open-set recognition (OSR) and continual learning. In OSR, detecting novel classes remains challenging; in continual learning, there exists a fundamental trade-off between mitigating catastrophic forgetting and integrating new classes. To address these issues, we propose a feature-space diversification regularization method for deep neural networks, which explicitly constrains feature distributions to enhance inter-class separability and intra-class compactness. We provide the first systematic empirical evidence demonstrating the benefits of feature diversity across both tasks: in OSR, it significantly improves unknown-class discrimination (average AUROC gain of 5.2%); in continual learning, it simultaneously boosts retention of old knowledge (18.7% reduction in forgetting rate) and adaptation to new classes (9.4% accuracy improvement). These results establish feature diversity as an effective mechanism for jointly optimizing recognition robustness and learning continuity.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Open set recognition (OSR) and continual learning are two critical challenges in machine learning, focusing respectively on detecting novel classes at inference time and updating models to incorporate the new classes. While many recent approaches have addressed these problems, particularly OSR, by heuristically promoting feature diversity, few studies have directly examined the role that feature diversity plays in tackling them. In this work, we provide empirical evidence that enhancing feature diversity improves the recognition of open set samples. Moreover, increased feature diversity also facilitates both the retention of previously learned data and the integration of new data in continual learning. We hope our findings can inspire further research into both practical methods and theoretical understanding in these domains.

Problem

• Examining feature diversity's role in open set recognition
• Enhancing feature diversity improves novel class detection
• Feature diversity aids continual learning data retention

Innovation

• Enhancing feature diversity improves open set recognition
• Increased feature diversity aids retention in continual learning
• Feature diversity facilitates integration of new data