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arXiv · 2110.15288

Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction

Konstantin Schürholt·Dimche Kostadinov·Damian Borth
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Abstract

Self-Supervised Learning (SSL) has been shown to learn useful and information-preserving representations. Neural Networks (NNs) are widely applied, yet their weight space is still not fully understood. Therefore, we propose to use SSL to learn hyper-representations of the weights of populations of NNs. To that end, we introduce domain specific data augmentations and an adapted attention architecture. Our empirical evaluation demonstrates that self-supervised representation learning in this domain is able to recover diverse NN model characteristics. Further, we show that the proposed learned representations outperform prior work for predicting hyper-parameters, test accuracy, and generalization gap as well as transfer to out-of-distribution settings.

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Konstantin Schürholt, Dimche Kostadinov, Damian Borth. 2021-10-28. Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction. https://arxiv.org/abs/2110.15288

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