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

We propose a new semi-supervised learning method on face-related tasks basedon Multi-Task Learning (MTL) and data distillation. The proposed methodexploits multiple datasets with different labels for different-but-relatedtasks such as simultaneous age, gender, race, facial expression estimation.Specifically, when there are only a few well-labeled data for a specific taskamong the multiple related ones, we exploit the labels of other related tasksin different domains. Our approach is composed of (1) a new MTL method whichcan deal with weakly labeled datasets and perform several tasks simultaneously,and (2) an MTL-based data distillation framework which enables networkgeneralization for the training and test data from different domains.Experiments show that the proposed multi-task system performs each task betterthan the baseline single task. It is also demonstrated that using differentdomain datasets along with the main dataset can enhance network generalizationand overcome the domain differences between datasets. Also, comparing datadistillation both on the baseline and MTL framework, the latter shows moreaccurate predictions on unlabeled data from different domains. Furthermore, byproposing a new learning-rate optimization method, our proposed network is ableto dynamically tune its learning rate.

Distill-2MD-MTL: Data Distillation based on Multi-Dataset Multi-Domain  Multi-Task Frame Work to Solve Face Related Tasksks, Multi Task Learning,  Semi-Supervised Learning