The task of continual learning requires careful design of algorithms that can tackle
catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario …

Supervised Continual learning involves updating a deep neural network (DNN) from an ever-
growing stream of labeled data. While most work has focused on overcoming catastrophic …

Continually learning in the real world must overcome many challenges, among which noisy
labels are a common and inevitable issue. In this work, we present a replay-based continual …

Continual learning usually assumes the incoming data are fully labeled, which might not be
applicable in real applications. In this work, we consider semi-supervised continual learning …

Self-supervised learning (SSL) has shown remarkable performance in computer vision tasks
when trained offline. However, in a Continual Learning (CL) scenario where new data is …

Learning under a continuously changing data distribution with incorrect labels is a desirable
real-world problem yet challenging. Large body of continual learning (CL) methods …

Catastrophic forgetting is a critical challenge in training deep neural networks. Although
continual learning has been investigated as a countermeasure to the problem, it often …

In the real world, humans have the ability to accumulate new knowledge in any conditions.
However, deeplearning suffers from the phenomenon so-called catastrophic forgetting of the …

The mainstream paradigm behind continual learning has been to adapt the model
parameters to non-stationary data distributions, where catastrophic forgetting is the central …

Continual learning is a setting where machine learning models learn novel concepts from
continuously shifting training data, while simultaneously avoiding degradation of knowledge …