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.

翻译：在大多数深度增量学习研究中，通常假设新样本已预先识别用于神经网络重新训练。然而，实际深度分类器常误判这些样本，导致错误预测，进而降低模型性能。开放集识别等技术可检测这些新样本，成为机器学习领域的重要研究方向。本文提出一种集成开放集识别的深度类增量学习框架，通过优化类增量学习特征以适配基于距离的开放集识别。实验结果表明，该方法在性能上优于当前最先进的增量学习技术，且在开放集识别中较基线方法展现出更优表现。