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 neural networks (DNNs) have been proven to have many redundancies.Hence, many efforts have been made to compress DNNs. However, the existingmodel compression methods treat all the input samples equally while ignoringthe fact that the difficulties of various input samples being correctlyclassified are different. To address this problem, DNNs with adaptive droppingmechanism are well explored in this work. To inform the DNNs how difficult theinput samples can be classified, a guideline that contains the information ofinput samples is introduced to improve the performance. Based on the developedguideline and adaptive dropping mechanism, an innovative soft-guidedadaptively-dropped (SGAD) neural network is proposed in this paper. Comparedwith the 32 layers residual neural networks, the presented SGAD can reduce theFLOPs by 77% with less than 1% drop in accuracy on CIFAR-10.

SGAD: Soft-Guided Adaptively-Dropped Neural Network