Search Results for author: Jiren Jin

Found 2 papers, 1 papers with code

Parameter Reference Loss for Unsupervised Domain Adaptation

no code implementations20 Nov 2017 Jiren Jin, Richard G. Calland, Takeru Miyato, Brian K. Vogel, Hideki Nakayama

Unsupervised domain adaptation (UDA) aims to utilize labeled data from a source domain to learn a model that generalizes to a target domain of unlabeled data.

Model Selection Unsupervised Domain Adaptation

Annotation Order Matters: Recurrent Image Annotator for Arbitrary Length Image Tagging

1 code implementation18 Apr 2016 Jiren Jin, Hideki Nakayama

In addition to comparing our model with existing methods using the conventional top-k evaluation measures, we also provide our model as a high quality baseline for the arbitrary length image tagging task.

Image Captioning Machine Translation +4

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