Continual learning aims to learn new tasks incrementally using less computation and
memory resources instead of retraining the model from scratch whenever new task arrives …

Uncertainty-based deep learning models have attracted a great deal of interest for their
ability to provide accurate and reliable predictions. Evidential deep learning stands out …

This paper proposes a working recipe of using Vision Transformer (ViT) in class incremental
learning. Although this recipe only combines existing techniques, developing the …

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 …

Intelligent robots need to recognize objects in their environment. This task is conceptually
different from the typical image classification task in computer vision. Robots need to …

Class-incremental continual learning is a core step towards developing artificial intelligence
systems that can continuously adapt to changes in the environment by learning new …

Abstract Class Incremental Learning (CIL), an indispensable ability for open-world
applications such as service robots, has received increasing attention in recent years …

Exemplar rehearsal-based methods with knowledge distillation (KD) have been widely used
in class incremental learning (CIL) scenarios. However, they still suffer from performance …

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 …

Most existing 3D object classification and retrieval algorithms rely on one-off supervised
learning on closed 3D object sets and tend to provide rigid convolutional neural networks …