Time series domain adaptation via contrastive adversarial domain disentangled network
Unsupervised domain adaptation is a machine learning framework to transform information learned from one or several source domains with many annotated samples to unlabeled target domains. A typical unsupervised domain adaptation method is typically designed base on visual data. Solutions on time se...
Saved in:
Main Author: | Huang, Xinyi |
---|---|
Other Authors: | Sinno Jialin Pan |
Format: | Thesis-Master by Research |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/168752 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Contrastive adversarial domain adaptation for machine remaining useful life prediction
by: Mohamed Ragab, et al.
Published: (2022) -
Unsupervised domain adaptation in the wild via disentangling representation learning
by: Li, Haoliang, et al.
Published: (2022) -
Multi-level adversarial network for domain adaptive semantic segmentation
by: Huang, Jiaxing, et al.
Published: (2022) -
Combating adversaries in network-structured security domains
by: Guo, Qingyu
Published: (2018) -
Multicomponent adversarial domain adaptation: a general framework
by: Yi, Chang'an, et al.
Published: (2023)