Continual learning (CL), or the ability to acquire, process, and learn from new information
without forgetting acquired knowledge, is a fundamental quality of an intelligent agent. The …

The capability of generalizing to out-of-distribution data is crucial for the deployment of
machine learning models in the real world. Existing domain generalization (DG) mainly …

Deep neural networks suffer from the catastrophic forgetting phenomenon when trained on
sequential tasks in continual learning, especially when data from previous tasks are …

Artificial learning systems aspire to mimic human intelligence by continually learning from a
stream of tasks without forgetting past knowledge. One way to enable such learning is to …

The prevalence of fake news on social media poses devastating and wide-ranging threats to
political beliefs, economic activities, and public health. Due to the continuous emergence of …

Training at the edge utilizes continuously evolving data generated at different locations.
Privacy concerns prohibit the co-location of this spatially as well as temporally distributed …

Existing continual image classification methods demonstrate that samples from all
sequences of continual classification tasks contain common (task-invariant) features and …

This paper develops a new method for lifelong learning referred to as Sparsify Dynamically
Expandable Network (SDEN) via Variational Dropout, which explores a sparse model while …

Machine {\em unlearning} has emerged as a prominent and challenging area of interest,
driven in large part by the rising regulatory demands for industries to delete user data upon …

In online continual learning, a neural network incrementally learns from a non-iid data
stream. Nearly all online continual learning methods employ experience replay to …