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 emerging edge computing has promoted immense interests in compacting aneural network without sacrificing much accuracy. In this regard, low-ranktensor decomposition constitutes a powerful tool to compress convolutionalneural networks (CNNs) by decomposing the 4-way kernel tensor into multi-stagesmaller ones. Building on top of Tucker-2 decomposition, we propose ageneralized Higher Order Tucker Articulated Kernels (HOTCAKE) scheme comprisingfour steps: input channel decomposition, guided Tucker rank selection, higherorder Tucker decomposition and fine-tuning. By subjecting each CONV layer toHOTCAKE, a highly compressed CNN model with graceful accuracy trade-off isobtained. Experiments show HOTCAKE can compress even pre-compressed models andproduce state-of-the-art lightweight networks.

HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression