1 research outputs found
Squared Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Data augmentation is one of the most popular techniques for improving the
robustness of neural networks. In addition to directly training the model with
original samples and augmented samples, a torrent of methods regularizing the
distance between embeddings/representations of the original samples and their
augmented counterparts have been introduced. In this paper, we explore these
various regularization choices, seeking to provide a general understanding of
how we should regularize the embeddings. Our analysis suggests the ideal
choices of regularization correspond to various assumptions. With an invariance
test, we argue that regularization is important if the model is to be used in a
broader context than the accuracy-driven setting because non-regularized
approaches are limited in learning the concept of invariance, despite equally
high accuracy. Finally, we also show that the generic approach we identified
(squared norm regularized augmentation) outperforms several recent
methods, which are each specially designed for one task and significantly more
complicated than ours, over three different tasks.Comment: 12 pages and an additional 9 pages as appendi