To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

Continual Learning (CL) investigates how to train Deep Networks on a stream of tasks
without incurring forgetting. CL settings proposed in literature assume that every incoming …

Despite significant advances, the performance of state-of-the-art continual learning
approaches hinges on the unrealistic scenario of fully labeled data. In this paper, we tackle …

Unsupervised Continual Learning (UCL) is a burgeoning field in machine learning, focusing
on enabling neural networks to sequentially learn tasks without explicit label information …

This study aimed to determine a fundamental method for the automated detection and
treatment of dental and orthodontic problems. Manual intervention is inefficient and prone to …

For generative autoencoders to learn a meaningful latent representation for data generation,
a careful balance must be achieved between reconstruction error and how close the …

In traditional machine learning, model training usually involves training the model from
scratch using the entire dataset. However, this approach often leads to catastrophic …

Abstract Artificial Neural Networks (ANNs) have been established as the centrepiece of
contemporary Artificial Intelligence, steadily raising the bar for what can be accomplished by …

L'apprentissage incrémental propose un nouveau paradigme d'apprentissage pour les
réseaux de neurones artificiels. Il vise à développer des systèmes capables d'enrichir leurs …

Incremental learning (IL) enables the adaptation of artificial agents to dynamic environments
in which data is presented in streams. This type of learning is needed when access to past …