C<sup>2</sup>DAN: An Improved Deep Adaptation Network with Domain Confusion and Classifier Adaptation
Deep neural networks have been successfully applied in domain adaptation which uses the labeled data of source domain to supplement useful information for target domain. Deep Adaptation Network (DAN) is one of these efficient frameworks, it utilizes Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to...
Main Authors: | Han Sun, Xinyi Chen, Ling Wang, Dong Liang, Ningzhong Liu, Huiyu Zhou |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-06-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/20/12/3606 |
Similar Items
-
A Two-stage Deep Domain Adaptation Method for Hyperspectral Image Classification
by: Zhaokui Li, et al.
Published: (2020-03-01) -
Unsupervised Domain Adaptation Based on Correlation Maximization
by: Lida Abdi, et al.
Published: (2021-01-01) -
Dynamic Distribution Adaptation Based Transfer Network for Cross Domain Bearing Fault Diagnosis
by: Yixiao Liao, et al.
Published: (2021-06-01) -
Domain adaptation for classifying disaster-related Twitter data
by: Sopova, Oleksandra
Published: (2017) -
Unsupervised Adversarial Domain Adaptation with Error‐Correcting Boundaries and Feature Adaption Metric for Remote‐Sensing Scene Classification
by: Chenhui Ma, et al.
Published: (2021-03-01)