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Publikation

Continual Learning Should Move Beyond Incremental Classification

Rupert Mitchell; Antonio Alliegro; Raffaello Camoriano; Dustin Carrión-Ojeda; Antonio Carta; Georgia Chalvatzaki; Nikhil Churamani; Carlo D'Eramo; Samin Hamidi; Robin Hesse; Fabian Hinder; Roshni Kamath; Vincenzo Lomonaco; Subarnaduti Paul; Francesca Pistilli; Tinne Tuytelaars; Gido M. van de Ven; Kristian Kersting; Simone Schaub-Meyer; Martin Mundt
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2502.11927, Pages 1-11, Computing Research Repository, 2025.

Zusammenfassung

Continual learning (CL) is the sub-field of ma- chine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental clas- sification tasks, where models learn to classify new categories while retaining knowledge of pre- viously learned ones. Here, we argue that main- taining such a focus limits both theoretical devel- opment and practical applicability of CL methods. Through a detailed analysis of concrete examples — including multi-target classification, robotics with constrained output spaces, learning in con- tinuous task domains, and higher-level concept memorization — we demonstrate how current CL approaches often fail when applied beyond stan- dard classification. We identify three fundamen- tal challenges: (C1) the nature of continuity in learning problems, (C2) the choice of appropri- ate spaces and metrics for measuring similarity, and (C3) the role of learning objectives beyond classification. For each challenge, we provide spe- cific recommendations to help move the field for- ward, including formalizing temporal dynamics through distribution processes, developing princi- pled approaches for continuous task spaces, and incorporating density estimation and generative objectives. In so doing, this position paper aims to broaden the scope of CL research while strength- ening its theoretical foundations, making it more applicable to real-world problems.

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