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

In recent years, large "in the wild" face datasets have been released in anattempt to facilitate progress in tasks such as face detection, facerecognition, and other tasks. Most of these datasets are acquired from webpageswith automatic procedures. As a consequence, noisy data are often found.Furthermore, in these large face datasets, the annotation of identities isimportant as they are used for training face recognition algorithms. But due tothe automatic way of gathering these datasets and due to their large size, manyidentities folder contain mislabeled samples which deteriorates the quality ofthe datasets. In this work, it is presented a semi-automatic method forcleaning the noisy large face datasets with the use of face recognition. Thismethodology is applied to clean the CelebA dataset and show its effectiveness.Furthermore, the list with the mislabelled samples in the CelebA dataset ismade available.

Dataset Cleaning -- A Cross Validation Methodology for Large Facial Datasets using Face Recognition