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arXiv · 2505.07450

Prototype Augmented Hypernetworks for Continual Learning

Neil De La Fuente·Maria Pilligua·Daniel Vidal·Albin Soutiff·Cecilia Curreli·Daniel Cremers·Andrey Barsky
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Abstract

Continual learning (CL) aims to learn a sequence of tasks without forgetting prior knowledge, but gradient updates for a new task often overwrite the weights learned earlier, causing catastrophic forgetting (CF). We propose Prototype-Augmented Hypernetworks (PAH), a framework where a single hypernetwork, conditioned on learnable task prototypes, dynamically generates task-specific classifier heads on demand. To mitigate forgetting, PAH combines cross-entropy with dual distillation losses, one to align logits and another to align prototypes, ensuring stable feature representations across tasks. Evaluations on Split-CIFAR100 and TinyImageNet demonstrate that PAH achieves state-of-the-art performance, reaching 74.5 % and 63.7 % accuracy with only 1.7 % and 4.4 % forgetting, respectively, surpassing prior methods without storing samples or heads.

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Neil De La Fuente, Maria Pilligua, Daniel Vidal, Albin Soutiff, Cecilia Curreli, Daniel Cremers, Andrey Barsky. 2025-05-12. Prototype Augmented Hypernetworks for Continual Learning. https://arxiv.org/abs/2505.07450

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