Artificial intelligence research has seen enormous progress over the past few decades, but it
predominantly relies on fixed datasets and stationary environments. Continual learning is an …

Continual learning (CL) aims to enable information systems to learn from a continuous data
stream across time. However, it is difficult for existing deep learning architectures to learn a …

Artificial neural networks thrive in solving the classification problem for a particular rigid task,
acquiring knowledge through generalized learning behaviour from a distinct training phase …

A continual learning agent learns online with a non-stationary and never-ending stream of
data. The key to such learning process is to overcome the catastrophic forgetting of …

Incremental learning (IL) is an important task aimed at increasing the capability of a trained
model, in terms of the number of classes recognizable by the model. The key problem in this …

Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks
when learning new ones. In this paper we focus on class incremental continual learning in …

The reviews had two major concerns: lack of a benchmarking on a complex dataset, and
unclear writing. To address these two major issues we: 1-Rewrote experiments section with …

Object understanding in egocentric visual data is arguably a fundamental research topic in
egocentric vision. However, existing object datasets are either non-egocentric or have …

In this paper, we introduce a challenging yet practical setting for person re-identification
(ReID) task, named lifelong person re-identification (LReID), which aims to continuously …

Popular deep-learning models in the field of image classification suffer from catastrophic
forgetting—models will forget previously acquired skills when learning new ones …