Transfer Neural Trees for Heterogeneous Domain Adaptation
By Wei-Yu Chen, Tzu-Ming Harry Hsu, Yao-Hung Hubert Tsai, Yu-Chiang Frank Wang, Ming-Syan Chen. 2016
93 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mei Wang
- Weihong Deng
- Gabriella Csurka
- Eric P. Xing
- Haohan Wang
- Vinay P. Namboodiri
- Zachary Chase Lipton
- Vinod Kumar Kurmi
In Computer Vision and Pattern Recognition
- Mei Wang
- Weihong Deng
- Gabriella Csurka
- Luc Van Gool
- Eric P. Xing
- Andrew Zisserman
- Yu Qiao
- Cordelia Schmid
Citations in the corpus
- Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, background): “hared and weakly-shared parameter layers. (Image courtesy X. Shu [157]). Heterogeneous deepDA. Concerning heterogeneous or multi-modal deep domain adaptation, we can mention the Transfer Neural Trees [156] proposed to relate heterogeneous cross-domain data. It is a two stream network, one stream for”
- Deep Visual Domain Adaptation: A Survey (2018, background): “[8] proposed transfer neural trees (TNTs), which consist of two stream networks to learn a domain-invariant feature representation for each modality.”