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

Active learning is commonly used to train label-efficient models byadaptively selecting the most informative queries. However, most activelearning strategies are designed to either learn a representation of the data(e.g., embedding or metric learning) or perform well on a task (e.g.,classification) on the data. However, many machine learning tasks involve acombination of both representation learning and a task-specific goal. Motivatedby this, we propose a novel unified query framework that can be applied to anyproblem in which a key component is learning a representation of the data thatreflects similarity. Our approach builds on similarity or nearest neighbor (NN)queries which seek to select samples that result in improved embeddings. Thequeries consist of a reference and a set of objects, with an oracle selectingthe object most similar (i.e., nearest) to the reference. In order to reducethe number of solicited queries, they are chosen adaptively according to aninformation theoretic criterion. We demonstrate the effectiveness of theproposed strategy on two tasks -- active metric learning and activeclassification -- using a variety of synthetic and real world datasets. Inparticular, we demonstrate that actively selected NN queries outperformrecently developed active triplet selection methods in a deep metric learningsetting. Further, we show that in classification, actively selecting classlabels can be reformulated as a process of selecting the most informative NNquery, allowing direct application of our method.

Active metric learning and classification using similarity queries