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 …

Deep learning for medical imaging suffers from temporal and privacy-related restrictions on
data availability. To still obtain viable models, continual learning aims to train in sequential …

Continual learning aims to provide intelligent agents capable of learning multiple tasks
sequentially with neural networks. One of its main challenging, catastrophic forgetting, is …

Using task-specific components within a neural network in continual learning (CL) is a
compelling strategy to address the stability-plasticity dilemma in fixed-capacity models …

Concept drift refers to a change in the data distribution affecting the data stream of future
samples. Consequently, learning models operating on the data stream might become …

Progressive learning is a deep learning framework in which tasks are learned sequentially,
with the capacity to leverage past knowledge from previously acquired tasks to aid in the …

Recently, people pay more attention to catastrophic forgetting problem, that is, the ability of
the model to recognize old tasks decreases dramatically when new tasks are added …

Continual learning agents should incrementally learn a sequence of tasks while satisfying
two main desiderata: accumulating on previous knowledge without forgetting and …

Learning multiple sequentially arriving tasks without forgetting previous knowledge, known
as Continual Learning (CL), remains a long-standing challenge for neural networks. Most …

Deep neural networks have achieved outstanding performance in many machine learning
tasks. However, this remarkable success is achieved in closed and static environments …