In contrast to batch learning where all training data is available at once, continual learning represents a family of methods that accumulate knowledge and learn continuously with data …
Incrementally learning new information from a non-stationary stream of data, referred to as 'continual learning', is a key feature of natural intelligence, but a challenging problem for …
Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in …
Y Guo, B Liu, D Zhao - International conference on machine …, 2022 - proceedings.mlr.press
This paper proposed a new online continual learning approach called OCM based on mutual information (MI) maximization. It achieves two objectives that are critical in dealing …
Online continual learning for image classification studies the problem of learning to classify images from an online stream of data and tasks, where tasks may include new classes …
Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning. In this paper, we first discuss …
Continual Learning (CL) aims to sequentially train models on streams of incoming data that vary in distribution by preserving previous knowledge while adapting to new data. Current …
CB Zhang, JW Xiao, X Liu, YC Chen… - Proceedings of the …, 2022 - openaccess.thecvf.com
In this work, we study the continual semantic segmentation problem, where the deep neural networks are required to incorporate new classes continually without catastrophic forgetting …
Z Mai, R Li, H Kim, S Sanner - Proceedings of the IEEE/CVF …, 2021 - openaccess.thecvf.com
Online class-incremental continual learning (CL) studies the problem of learning new classes continually from an online non-stationary data stream, intending to adapt to new …