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 …

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 …

Despite the significant recent progress made on 3D point cloud semantic segmentation, the
current methods require training data for all classes at once, and are not suitable for real-life …

Continual Named Entity Recognition (CNER) is a burgeoning area, which involves updating
an existing model by incorporating new entity types sequentially. Nevertheless, continual …

Background The implementation of deep learning models for medical image classification
poses significant challenges, including gradual performance degradation and limited …

Prior work on few-shot class incremental learning has operated with an unnatural
assumption: the number of ways and number of shots are assumed to be known and fixed …

A fingerprint region of interest (roi) segmentation algorithm is designed to separate the
foreground fingerprint from the background noise. All the learning based state-of-the-art …

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

The effectiveness of fingerprint-based authentication systems on good quality fingerprints is
established long back. However, the performance of standard fingerprint matching systems …

Abstract In the Class Incremental Learning (CIL) setup, a learning model must have the
ability to incrementally update its knowledge to recognize newly appeared classes …