output
20142025
most citedDivide and not forget: Ensemble of selectively trained experts in Continual Learning

11 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing 2024Show all

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cs.CV2024

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…

cs.LG2024★ 11 cited

Divide and not forget: Ensemble of selectively trained experts in Continual Learning

Grzegorz Rypeść, Sebastian Cygert, Valeriya Khan +3

Class-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know. A trend in this area is to use a mixtu…