English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Data augmentation is a popular pre-processing trick to improve generalizationaccuracy. It is believed that by processing augmented inputs in tandem with theoriginal ones, the model learns a more robust set of features which are sharedbetween the original and augmented counterparts. However, we show that is notthe case even for the best augmentation technique. In this work, we take aDomain Generalization viewpoint of augmentation based methods. This newperspective allowed for probing overfitting and delineating avenues forimprovement. Our exploration with the state-of-art augmentation method providesevidence that the learned representations are not as robust even towardsdistortions used during training. This suggests evidence for the untappedpotential of augmented examples.

Untapped Potential of Data Augmentation: A Domain Generalization Viewpoint