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

Existing fine-tuning methods use a single learning rate over all layers. Inthis paper, first, we discuss that trends of layer-wise weight variations byfine-tuning using a single learning rate do not match the well-known notionthat lower-level layers extract general features and higher-level layersextract specific features. Based on our discussion, we propose an algorithmthat improves fine-tuning performance and reduces network complexity throughlayer-wise pruning and auto-tuning of layer-wise learning rates. The proposedalgorithm has verified the effectiveness by achieving state-of-the-artperformance on the image retrieval benchmark datasets (CUB-200, Cars-196,Stanford online product, and Inshop). Code is available athttps://github.com/youngminPIL/AutoLR.

AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks