584 citations · 2.3k across the 78 of their papers we have counts for
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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…
Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning
Alex Gomez-Villa, Bartlomiej Twardowski, Kai Wang +1
Continuous unsupervised representation learning (CURL) research has greatly benefited from improvements in self-supervised learning (SSL) techniques. As a result, existing CURL met…
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…
Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning
Antonio Carta, Andrea Cossu, Vincenzo Lomonaco +2
Distributed learning on the edge often comprises self-centered devices (SCD) which learn local tasks independently and are unwilling to contribute to the performance of other SDCs.…
ICICLE: Interpretable Class Incremental Continual Learning
Dawid Rymarczyk, Joost van de Weijer, Bartosz Zieliński +1
Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on b…
Towards Label-Efficient Incremental Learning: A Survey
Mert Kilickaya, Joost van de Weijer, Yuki M. Asano
The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence. Such an approach is non-incremental, as it assume…
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning
Javad Zolfaghari Bengar, Bogdan Raducanu, Joost van de Weijer
Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring th…
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…
Class-incremental learning: survey and performance evaluation on image classification
Marc Masana, Xialei Liu, Bartlomiej Twardowski +3
For future learning systems, incremental learning is desirable because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new…