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

This paper addresses a boosting method for mapping functionality of neuralnetworks in visual recognition such as image classification and facerecognition. We present reversible learning for generating and learning latentfeatures using the network itself. By generating latent features correspondingto hard samples and applying the generated features in a training stage,reversible learning can improve a mapping functionality without additional dataaugmentation or handling the bias of dataset. We demonstrate an efficiency ofthe proposed method on the MNIST,Cifar-10/100, and Extremely Biased and poorlycategorized dataset (EBPC dataset). The experimental results show that theproposed method can outperform existing state-of-the-art methods in visualrecognition. Extensive analysis shows that our method can efficiently improvethe mapping capability of a network.

Boosting Mapping Functionality of Neural Networks via Latent Feature Generation based on Reversible Learning