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

This notebook paper presents an overview and comparative analysis of oursystems designed for the following two tasks in Visual Domain AdaptationChallenge (VisDA-2019): multi-source domain adaptation and semi-superviseddomain adaptation.  Multi-Source Domain Adaptation: We investigate both pixel-level andfeature-level adaptation for multi-source domain adaptation task, i.e.,directly hallucinating labeled target sample via CycleGAN and learningdomain-invariant feature representations through self-learning. Moreover, themechanism of fusing features from different backbones is further studied tofacilitate the learning of domain-invariant classifiers. Source code andpre-trained models are available at\url{https://github.com/Panda-Peter/visda2019-multisource}.  Semi-Supervised Domain Adaptation: For this task, we adopt a standardself-learning framework to construct a classifier based on the labeled sourceand target data, and generate the pseudo labels for unlabeled target data.These target data with pseudo labels are then exploited to re-training theclassifier in a following iteration. Furthermore, a prototype-basedclassification module is additionally utilized to strengthen the predictions.Source code and pre-trained models are available at\url{https://github.com/Panda-Peter/visda2019-semisupervised}.

Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019