Keywords: Weight Generation, Representation Learning, Model Zoo, Hyper-Representations, Ensembling
TL;DR: We extend hyper-representations for generative use to sample neural network weights for initialization, ensembling and transfer learning.
Abstract: Learning representations of neural network weights given a model zoo is an emerg- ing and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation. Recently, an autoencoder trained on a model zoo was able to learn a hyper-representation, which captures intrinsic and extrinsic properties of the models in the zoo. In this work, we ex- tend hyper-representations for generative use to sample new model weights. We propose layer-wise loss normalization which we demonstrate is key to generate high-performing models and several sampling methods based on the topology of hyper-representations. The models generated using our methods are diverse, per- formant and capable to outperform strong baselines as evaluated on several down- stream tasks: initialization, ensemble sampling and transfer learning. Our results indicate the potential of knowledge aggregation from model zoos to new models via hyper-representations thereby paving the avenue for novel research directions.
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