6 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…
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.…
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…
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…
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…
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…