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.

本文介绍了一种深度类增量学习框架，与开放集识别相结合，通过精炼增量学习特征来适应基于距离的开放集识别。实验结果验证了我们的方法优于现有的增量学习技术，并在开放集识别方面表现出比基准方法更优异的性能。

OpenIncrement：一种统一的开放集识别和深度增量学习框架

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

A practical issue of edge AI systems is that data distributions of trained
dataset and deployed environment may differ due to noise and environmental
changes over time. Such a phenomenon is known as a concept drift, and this gap
degrades the performance of edge AI systems and may introduce system failures.
To address this gap, retraining of neural network models triggered by concept
drift detection is a practical approach. However, since available compute
resources are strictly limited in edge devices, in this paper we propose a
fully sequential concept drift detection method in cooperation with an
on-device sequential learning technique of neural networks. In this case, both
the neural network retraining and the proposed concept drift detection are done
only by sequential computation to reduce computation cost and memory
utilization. Evaluation results of the proposed approach shows that while the
accuracy is decreased by 3.8%-4.3% compared to existing batch-based detection
methods, it decreases the memory size by 88.9%-96.4% and the execution time by
1.3%-83.8%. As a result, the combination of the neural network retraining and
the proposed concept drift detection method is demonstrated on Raspberry Pi
Pico that has 264kB memory.

本文提出一种边缘 AI 系统中的概念漂移检测方法，通过序列计算实现神经网络的重新训练和检测概念漂移，降低计算和内存使用，实现在 Raspberry Pi Pico 等资源受限的设备上的应用。