Haebeom Lee

MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures

2020 · arXiv (Cornell University)

Regularization and transfer learning are two popular techniques to enhance generalization on unseen data, which is a fundamental problem of machine learning. Regularization techniques are versatile, as they are task- and architecture-agnostic, but they do …