OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning

• URL: http://arxiv.org/abs/2310.03848v1
• Date: Thu, 5 Oct 2023 19:08:08 GMT
• Title: OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning
• Authors: Jiawen Xu, Claas Grohnfeldt, Odej Kao
• Abstract summary: We introduce a deep class-incremental learning framework integrated with open set recognition. Our approach refines class-incrementally learned features to adapt them for distance-based open set recognition. Experimental results validate that our method outperforms state-of-the-art incremental learning techniques.
• Score: 4.278434830731282
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In most works on deep incremental learning research, it is assumed that novel samples are pre-identified for neural network retraining. However, practical deep classifiers often misidentify these samples, leading to erroneous predictions. Such misclassifications can degrade model performance. Techniques like open set recognition offer a means to detect these novel samples, representing a significant area in the machine learning domain. In this paper, we introduce a deep class-incremental learning framework integrated with open set recognition. Our approach refines class-incrementally learned features to adapt them for distance-based open set recognition. Experimental results validate that our method outperforms state-of-the-art incremental learning techniques and exhibits superior performance in open set recognition compared to baseline methods.

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