Prototype Augmented Hypernetworks for Continual Learning

2025-05-12

AI Summary

Catastrophic forgetting in continual learning arises when gradient updates for new tasks overwrite previously acquired knowledge. To address this, we propose Prototype-Augmented Hypernetworks (PAH), the first framework to integrate learnable task prototypes with a hypernetwork for sample-free, classification-head-free dynamic head generation. PAH introduces a dual-distillation loss: logits distillation preserves output consistency across tasks, while prototype alignment distillation stabilizes the shared feature space across tasks. The model is jointly optimized via cross-entropy and distillation losses. On Split-CIFAR100 and TinyImageNet, PAH achieves average accuracies of 74.5% and 63.7%, respectively, with forgetting rates of only 1.7% and 4.4%. These results significantly surpass those of existing replay-free methods, establishing a novel lightweight paradigm for continual learning.

Technology Category

Machine Learning: Life-Long and Continual LearningSearch and Optimization: Learning to SearchComputer Vision: Learning & Optimization for CV

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingEconomics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labelingSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Prevent catastrophic forgetting in continual learning tasks
• Dynamically generate task-specific classifier heads via hypernetworks
• Achieve high accuracy with minimal forgetting across datasets

Innovation

• Hypernetwork generates task-specific heads dynamically
• Dual distillation losses mitigate catastrophic forgetting
• Task prototypes ensure stable feature representations