Topologically Densified Distributions
Christoph Hofer · Florian Graf · Marc Niethammer · Roland Kwitt
Keywords:
Deep Learning Theory
Representation Learning
Statistical Learning Theory
Supervised Learning
Deep Learning - Theory
2020 Poster
Abstract
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.
Video
Chat is not available.
Successful Page Load