Topologically Densified Distributions

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Christoph Hofer, Florian Graf, Marc Niethammer, Roland Kwitt


<p>We study regularization in the context of small sample-size learning with over-parametrized neural networks. Specifically, we shift focus from architectural properties, such as norms on the network weights, to properties of the internal representations before a linear classifier. Specifically, we impose a topological constraint on samples drawn from the probability measure induced in that space. This provably leads to mass concentration effects around the representations of training instances, i.e., a property beneficial for generalization. By leveraging previous work to impose topological constrains in a neural network setting, we provide empirical evidence (across various vision benchmarks) to support our claim for better generalization.</p>