Hypernetworks

Hypernetworks are neural networks whose primary purpose is to generate the parameters of another network. In a typical configuration, a hypernetwork H takes a conditioning input — for example a task descriptor, style vector, or context — and outputs the weights and biases of a separate target network N. During inference, N uses the generated parameters to compute its outputs, effectively becoming a dynamic or context-dependent model.

Implementation variations include generating all weights of N, only a subset (such as a layer or block), or a compact, factorized representation that is expanded on demand. Some designs have H produce scale and shift parameters for feature-wise modulation (FiLM-like conditioning) rather than entire weight matrices. The whole system is trained end-to-end by backpropagating through both H and N.

Origin and use cases: the concept was introduced in 2016 by Ha, Dai, and Le as a way to share information across tasks while enabling rapid adaptation. Hypernetworks have since been explored for meta-learning, few-shot learning, multi-task learning, domain adaptation, and model compression, where a single hypernetwork can parameterize many related networks or contexts.

Advantages include parameter sharing and dynamic specialization without storing separate full parameter sets for each task. They can enable fast adaptation to new inputs or domains and reduce memory by reusing a single hypernetwork. Limitations include increased computational cost, potential optimization instability, and a risk of overfitting when the hypernetwork must generate large or highly variable weights. Design choices such as what to generate and how to regularize are important for practical performance.

Related concepts include dynamic or conditional computation and other meta-learning approaches that adapt model parameters on the fly. Hypernetworks remain an area of active research, balancing expressiveness with efficiency.