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

The aim of Few-Shot learning methods is to train models which can easilyadapt to previously unseen tasks, based on small amounts of data. One of themost popular and elegant Few-Shot learning approaches is Model-AgnosticMeta-Learning (MAML). The main idea behind this method is to learn the generalweights of the meta-model, which are further adapted to specific problems in asmall number of gradient steps. However, the model's main limitation lies inthe fact that the update procedure is realized by gradient-based optimisation.In consequence, MAML cannot always modify weights to the essential level in oneor even a few gradient iterations. On the other hand, using many gradient stepsresults in a complex and time-consuming optimization procedure, which is hardto train in practice, and may lead to overfitting. In this paper, we proposeHyperMAML, a novel generalization of MAML, where the training of the updateprocedure is also part of the model. Namely, in HyperMAML, instead of updatingthe weights with gradient descent, we use for this purpose a trainableHypernetwork. Consequently, in this framework, the model can generatesignificant updates whose range is not limited to a fixed number of gradientsteps. Experiments show that HyperMAML consistently outperforms MAML andperforms comparably to other state-of-the-art techniques in a number ofstandard Few-Shot learning benchmarks.

HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks