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

Recent research on Generalized Zero-Shot Learning (GZSL) has focusedprimarily on generation-based methods. However, current literature hasoverlooked the fundamental principles of these methods and has made limitedprogress in a complex manner. In this paper, we aim to deconstruct thegenerator-classifier framework and provide guidance for its improvement andextension. We begin by breaking down the generator-learned unseen classdistribution into class-level and instance-level distributions. Through ouranalysis of the role of these two types of distributions in solving the GZSLproblem, we generalize the focus of the generation-based approach, emphasizingthe importance of (i) attribute generalization in generator learning and (ii)independent classifier learning with partially biased data. We present a simplemethod based on this analysis that outperforms SotAs on four public GZSLdatasets, demonstrating the validity of our deconstruction. Furthermore, ourproposed method remains effective even without a generative model, representinga step towards simplifying the generator-classifier structure. Our code isavailable at \url{https://github.com/cdb342/DGZ}.

Deconstructed Generation-Based Zero-Shot Model