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

Metric-based few-shot learning methods try to overcome the difficulty due tothe lack of training examples by learning embedding to make comparison easy. Wepropose a novel algorithm to generate class representatives for few-shotclassification tasks. As a probabilistic model for learned features of inputs,we consider a mixture of von Mises-Fisher distributions which is known to bemore expressive than Gaussian in a high dimensional space. Then, from adiscriminative classifier perspective, we get a better class representativeconsidering inter-class correlation which has not been addressed byconventional few-shot learning algorithms. We apply our method to\emph{mini}ImageNet and \emph{tiered}ImageNet datasets, and show that theproposed approach outperforms other comparable methods in few-shotclassification tasks.

Discriminative Few-Shot Learning Based on Directional Statistics