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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

How can we reuse existing knowledge, in the form of available datasets, whensolving a new and apparently unrelated target task from a set of unlabeleddata? In this work we make a first contribution to answer this question in thecontext of image classification. We frame this quest as an active learningproblem and use zero-shot classifiers to guide the learning process by linkingthe new task to the existing classifiers. By revisiting the dual formulation ofadaptive SVM, we reveal two basic conditions to choose greedily only the mostrelevant samples to be annotated. On this basis we propose an effective activelearning algorithm which learns the best possible target classification modelwith minimum human labeling effort. Extensive experiments on two challengingdatasets show the value of our approach compared to the state-of-the-art activelearning methodologies, as well as its potential to reuse past datasets withminimal effort for future tasks.

Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks