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

Few-shot models aim at making predictions using a minimal number of labeledexamples from a given task. The main challenge in this area is the one-shotsetting where only one element represents each class. We propose HyperShot -the fusion of kernels and hypernetwork paradigm. Compared to referenceapproaches that apply a gradient-based adjustment of the parameters, our modelaims to switch the classification module parameters depending on the task'sembedding. In practice, we utilize a hypernetwork, which takes the aggregatedinformation from support data and returns the classifier's parametershandcrafted for the considered problem. Moreover, we introduce the kernel-basedrepresentation of the support examples delivered to hypernetwork to create theparameters of the classification module. Consequently, we rely on relationsbetween embeddings of the support examples instead of direct feature valuesprovided by the backbone models. Thanks to this approach, our model can adaptto highly different tasks.

HyperShot: Few-Shot Learning by Kernel HyperNetworks