557 citations · 832 across the 25 of their papers we have counts for
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Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization
Sebastian Dziadzio, Çağatay Yıldız, Gido M. van de Ven +3
The ability of machine learning systems to learn continually is hindered by catastrophic forgetting, the tendency of neural networks to overwrite previously acquired knowledge when…
Continual Learning of Diffusion Models with Generative Distillation
Sergi Masip, Pau Rodriguez, Tinne Tuytelaars +1
Diffusion models are powerful generative models that achieve state-of-the-art performance in image synthesis. However, training them demands substantial amounts of data and computa…
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…
Two Complementary Perspectives to Continual Learning: Ask Not Only What to Optimize, But Also How
Timm Hess, Tinne Tuytelaars, Gido M. van de Ven
Recent years have seen considerable progress in the continual training of deep neural networks, predominantly thanks to approaches that add replay or regularization terms to the lo…
Prediction Error-based Classification for Class-Incremental Learning
Michał Zając, Tinne Tuytelaars, Gido M. van de Ven
Class-incremental learning (CIL) is a particularly challenging variant of continual learning, where the goal is to learn to discriminate between all classes presented in an increme…
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…