The ability of artificial agents to increment their capabilities when confronted with new data is
an open challenge in artificial intelligence. The main challenge faced in such cases is …

In contrast to batch learning where all training data is available at once, continual learning
represents a family of methods that accumulate knowledge and learn continuously with data …

Deep learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical
application of DL in IoT is device identification from wireless signals, namely …

Incrementally training deep neural networks to recognize new classes is a challenging
problem. Most existing class-incremental learning methods store data or use generative …

Incremental Learning (IL) aims to develop Machine Learning (ML) models that can learn
from continuous streams of data and mitigate catastrophic forgetting. We analyse the current …

Due to the differences in the feature distribution between classes, when the model learns in
a continuous data stream, it will encounter catastrophic forgetting. The incremental learning …

Different from fine-tuning models pre-trained on a large-scale dataset of preset classes,
class-incremental learning (CIL) aims to recognize novel classes over time without forgetting …

Abstract We motivate Energy-Based Models (EBMs) as a promising model class for
continual learning problems. Instead of tackling continual learning via the use of external …

Deep Learning models have achieved remarkable performance in tasks such as image
classification or generation, often surpassing human accuracy. However, they can struggle …

Incremental learning enables artificial agents to learn from sequential data. While important
progress was made by exploiting deep neural networks, incremental learning remains very …