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

Utilizing well-trained representations in transfer learning often results insuperior performance and faster convergence compared to training from scratch.However, even if such good representations are transferred, a model can easilyoverfit the limited training dataset and lose the valuable properties of thetransferred representations. This phenomenon is more severe in ViT due to itslow inductive bias. Through experimental analysis using attention maps in ViT,we observe that the rich representations deteriorate when trained on a smalldataset. Motivated by this finding, we propose a novel and simpleregularization method for ViT called Guided Transfer of spatial Attention(GTA). Our proposed method regularizes the self-attention maps between thesource and target models. A target model can fully exploit the knowledgerelated to object localization properties through this explicit regularization.Our experimental results show that the proposed GTA consistently improves theaccuracy across five benchmark datasets especially when the number of trainingdata is small.

GTA: Guided Transfer of Spatial Attention from Object-Centric Representations