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

Deep representation learning using triplet network for classification suffersfrom a lack of theoretical foundation and difficulty in tuning both the networkand classifiers for performance. To address the problem, local-margin tripletloss along with local positive and negative mining strategy is proposed withtheory on how the strategy integrate nearest-neighbor hyper-parameter withtriplet learning to increase subsequent classification performance. Results inexperiments with 2 public datasets, MNIST and Cifar-10, and 2 small medicalimage datasets demonstrate that proposed strategy outperforms end-to-endsoftmax and typical triplet loss in settings without data augmentation whilemaintaining utility of transferable feature for related tasks. The methodserves as a good performance baseline where end-to-end methods encounterdifficulties such as small sample data with limited allowable dataaugmentation.

Neighborhood Watch: Representation Learning with Local-Margin Triplet Loss and Sampling Strategy for K-Nearest-Neighbor Image Classification