Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that the …

机器学习技术成功地应用于计算机视觉, 自然语言处理和语音识别等众多领域. 然而,
现有的大多数机器学习模型在部署后类别和参数是固定的, 只能泛化到训练集中出现的类别 …

Deep learning methods are criticized for the requirement of large set of labeled training data.
Therefore, few-shot learning (FSL), which enables fast learning with only a few labeled …

Class-incremental learning (CIL) aims to recognize classes that emerged in different
phases. The joint-training (JT), which trains the model jointly with all classes, is often …

Class-incremental learning (CIL) aims to recognize new classes incrementally while
maintaining the discriminability of old classes. Most existing CIL methods are exemplar …

Existing machine learning models can well handle common classes but struggle to detect
unfamiliar or unknown classes due to environmental variations. To address this challenge …

Deep learning technology has played an important role in our life. Since deep learning
technology relies on the neural network model, it is still plagued by the catastrophic …

Class Incremental Learning (CIL) is a promising approach to addressing the catastrophic
forgetting problem when learning for new categories. Though recent works based on …