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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The dichotomy between the challenging nature of obtaining annotations foractivities, and the more straightforward nature of data collection fromwearables, has resulted in significant interest in the development oftechniques that utilize large quantities of unlabeled data for learningrepresentations. Contrastive Predictive Coding (CPC) is one such method,learning effective representations by leveraging properties of time-series datato setup a contrastive future timestep prediction task. In this work, wepropose enhancements to CPC, by systematically investigating the encoderarchitecture, the aggregator network, and the future timestep prediction,resulting in a fully convolutional architecture, thereby improvingparallelizability. Across sensor positions and activities, our method showssubstantial improvements on four of six target datasets, demonstrating itsability to empower a wide range of application scenarios. Further, in thepresence of very limited labeled data, our technique significantly outperformsboth supervised and self-supervised baselines, positively impacting situationswhere collecting only a few seconds of labeled data may be possible. This ispromising, as CPC does not require specialized data transformations orreconstructions for learning effective representations.

Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition