Supervised representation learning with double encoding-layer autoencoder for transfer learning
Transfer learning has gained a lot of attention and interest in the past decade. One crucial research issue in transfer learning is how to find a good representation for instances of different domains such that the divergence between domains can be reduced with the new representation. Recently, deep...
Saved in:
Main Authors: | Zhuang, Fuzhen, Cheng, Xiaohu, Luo, Ping, Pan, Sinno Jialin, He, Qing |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/143455 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Disentangled variational auto-encoder for semi-supervised learning
by: Li, Yang, et al.
Published: (2021) -
Learning to iteratively solve routing problems with dual-aspect collaborative transformer
by: MA, Yining, et al.
Published: (2021) -
Age-related differences in neural activity for novelty and relational encoding of scenes
by: Leow, Wei Yang Dayton
Published: (2015) -
Tactile classification with supervise autoencoder and joint learning
by: Gao, Ruihan
Published: (2020) -
Inwards buildup of concentric polymer layers: A method for biomolecule encapsulation and microcapsule encoding
by: Bai, J., et al.
Published: (2014)