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

Theoretically, domain adaptation is a well-researched problem. Further, thistheory has been well-used in practice. In particular, we note the bound ontarget error given by Ben-David et al. (2010) and the well-knowndomain-aligning algorithm based on this work using Domain Adversarial NeuralNetworks (DANN) presented by Ganin and Lempitsky (2015). Recently, multiplevariants of DANN have been proposed for the related problem of domaingeneralization, but without much discussion of the original motivating bound.In this paper, we investigate the validity of DANN in domain generalizationfrom this perspective. We investigate conditions under which application ofDANN makes sense and further consider DANN as a dynamic process duringtraining. Our investigation suggests that the application of DANN to domaingeneralization may not be as straightforward as it seems. To address this, wedesign an algorithmic extension to DANN in the domain generalization case. Ourexperimentation validates both theory and algorithm.

Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve