6 citations · 12 across the 8 of their papers we have counts for
8 papers
EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning
Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais +2
Exemplar-free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold…
The Expanding Scope of the Stability Gap: Unveiling its Presence in Joint Incremental Learning of Homogeneous Tasks
Sandesh Kamath, Albin Soutif-Cormerais, Joost van de Weijer +1
Recent research identified a temporary performance drop on previously learned tasks when transitioning to a new one. This drop is called the stability gap and has great consequence…
Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning
Dipam Goswami, Albin Soutif--Cormerais, Yuyang Liu +3
Continual learning methods are known to suffer from catastrophic forgetting, a phenomenon that is particularly hard to counter for methods that do not store exemplars of previous t…
An Empirical Analysis of Forgetting in Pre-trained Models with Incremental Low-Rank Updates
Albin Soutif--Cormerais, Simone Magistri, Joost van de Weijer +1
Broad, open source availability of large pretrained foundation models on the internet through platforms such as HuggingFace has taken the world of practical deep learning by storm.…
Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental Learning
Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais +2
Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold…
A Comprehensive Empirical Evaluation on Online Continual Learning
Albin Soutif--Cormerais, Antonio Carta, Andrea Cossu +4
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 a…
Improving Online Continual Learning Performance and Stability with Temporal Ensembles
Albin Soutif--Cormerais, Antonio Carta, Joost Van de Weijer
Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an onlin…
On the importance of cross-task features for class-incremental learning
Albin Soutif--Cormerais, Marc Masana, Joost van de Weijer +1
In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constrain…