The field of deep learning has witnessed significant progress, particularly in computer vision
(CV), natural language processing (NLP), and speech. The use of large-scale models …

Online continual learning aims to get closer to a live learning experience by learning directly
on a stream of data with temporally shifting distribution and by storing a minimum amount of …

Continual Learning (CL) focuses on maximizing the predictive performance of a model
across a non-stationary stream of data. Unfortunately CL models tend to forget previous …

Fine-grained visual classification (FGVC) is a difficult task due to the challenges of
discriminative feature learning. Most existing methods directly use the final output of the …

Online continual learning aims to get closer to a live learning experience by learning directly
on a stream of data with temporally shifting distribution and by storing a minimum amount of …

Continual Learning (CL) trains models on streams of data, with the aim of learning new
information without forgetting previous knowledge. However, many of these models lack …

Continual learning (CL) provides a framework for training models in ever-evolving
environments. Although re-occurrence of previously seen objects or tasks is common in real …

Driven by the progress in efficient embedded processing, there is an accelerating trend
toward running machine learning models directly on wearable Brain-Machine Interfaces …

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 …

TPC (Three-Phase Consolidation) is here introduced as a simple but effective approach to
continually learn new classes (and/or instances of known classes) while controlling …