Measuring Generalization with Optimal TransportDownload PDF

May 21, 2021 (edited Oct 26, 2021)NeurIPS 2021 SpotlightReaders: Everyone
  • Keywords: generalization, optimal transport, representation
  • TL;DR: We propose a generalization measure based on optimal transport.
  • Abstract: Understanding the generalization of deep neural networks is one of the most important tasks in deep learning. Although much progress has been made, theoretical error bounds still often behave disparately from empirical observations. In this work, we develop margin-based generalization bounds, where the margins are normalized with optimal transport costs between independent random subsets sampled from the training distribution. In particular, the optimal transport cost can be interpreted as a generalization of variance which captures the structural properties of the learned feature space. Our bounds robustly predict the generalization error, given training data and network parameters, on large scale datasets. Theoretically, we demonstrate that the concentration and separation of features play crucial roles in generalization, supporting empirical results in the literature.
  • Supplementary Material: pdf
  • Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
  • Code: https://github.com/chingyaoc/kV-Margin
13 Replies

Loading