17 citations · 24 across the 6 of their papers we have counts for
6 papers
Same accuracy, twice as fast: continuous training surpasses retraining from scratch
Eli Verwimp, Guy Hacohen, Tinne Tuytelaars
Continual learning aims to enable models to adapt to new datasets without losing performance on previously learned data, often assuming that prior data is no longer available. Howe…
Continual Learning: Applications and the Road Forward
Eli Verwimp, Rahaf Aljundi, Shai Ben-David +17
Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting wh…
Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting
Timm Hess, Eli Verwimp, Gido M. van de Ven +1
Continual learning research has shown that neural networks suffer from catastrophic forgetting "at the output level", but it is debated whether this is also the case at the level o…
CLAD: A realistic Continual Learning benchmark for Autonomous Driving
Eli Verwimp, Kuo Yang, Sarah Parisot +5
In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classificatio…
Re-examining Distillation For Continual Object Detection
Eli Verwimp, Kuo Yang, Sarah Parisot +5
Training models continually to detect and classify objects, from new classes and new domains, remains an open problem. In this work, we conduct a thorough analysis of why and how o…
Rehearsal revealed: The limits and merits of revisiting samples in continual learning
Eli Verwimp, Matthias De Lange, Tinne Tuytelaars
Learning from non-stationary data streams and overcoming catastrophic forgetting still poses a serious challenge for machine learning research. Rather than aiming to improve state-…