Hypernetworks for Dynamic Weight Generation in Few-Shot Learning

Abstract

Hypernetworks—neural networks that generate the weights of a target network—offer a compelling paradigm for rapid task adaptation by producing task-specific parameters in a single forward pass, bypassing the need for iterative gradient-based fine-tuning. This paper presents a comprehensive examination of hypernetwork architectures for few-shot learning, spanning theoretical foundations, architectural design principles, and empirical performance across standard benchmarks. We trace the evolution from foundational hypernetwork formulations through task-conditioned and attention-based variants to modern approaches integrating hypernetworks with pretrained foundation models via prompt generation and low-rank adaptation. We provide detailed comparisons with optimization-based meta-learning (MAML), metric learning (Prototypical Networks), and amortized inference approaches. Our analysis covers both classification and regression settings, addressing challenges including weight space dimensionality, generalization bounds, and computational efficiency. We identify key open problems including scaling hypernetworks to generate weights for billion-parameter models and establishing tighter theoretical guarantees for hypernetwork-generated parameters.