Gradient representations in ReLU networks as similarity functions

Dániel Rácz; Bálint Daróczy

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arXiv · 2110.13581

Gradient representations in ReLU networks as similarity functions

Dániel Rácz·Bálint Daróczy

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Abstract

Feed-forward networks can be interpreted as mappings with linear decision surfaces at the level of the last layer. We investigate how the tangent space of the network can be exploited to refine the decision in case of ReLU (Rectified Linear Unit) activations. We show that a simple Riemannian metric parametrized on the parameters of the network forms a similarity function at least as good as the original network and we suggest a sparse metric to increase the similarity gap.

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Dániel Rácz, Bálint Daróczy. 2021-10-26. Gradient representations in ReLU networks as similarity functions. https://arxiv.org/abs/2110.13581

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