Similarity-based meta-learning network with adversarial domain adaptation for cross-domain fault identification

2021 ◽  
Vol 217 ◽  
pp. 106829
Author(s):  
Yong Feng ◽  
Jinglong Chen ◽  
Zhuozheng Yang ◽  
Xiaogang Song ◽  
Yuanhong Chang ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Fault Identification ◽  
Learning Network ◽  
Cross Domain ◽  
Meta Learning

scholarly journals Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning

2021 ◽  
Vol 11 (24) ◽  
pp. 12037
Author(s):  
Xiaoyu Hou ◽  
Jihui Xu ◽  
Jinming Wu ◽  
Huaiyu Xu
Keyword(s):  
Feature Extraction ◽  
Supervised Learning ◽  
Density Estimation ◽  
Real World ◽  
Domain Adaptation ◽  
Manual Annotation ◽  
Crowd Counting ◽  
Cross Domain ◽  
Meta Learning ◽  
Density Map

Counting people in crowd scenarios is extensively conducted in drone inspections, video surveillance, and public safety applications. Today, crowd count algorithms with supervised learning have improved significantly, but with a reliance on a large amount of manual annotation. However, in real world scenarios, different photo angles, exposures, location heights, complex backgrounds, and limited annotation data lead to supervised learning methods not working satisfactorily, plus many of them suffer from overfitting problems. To address the above issues, we focus on training synthetic crowd data and investigate how to transfer information to real-world datasets while reducing the need for manual annotation. CNN-based crowd-counting algorithms usually consist of feature extraction, density estimation, and count regression. To improve the domain adaptation in feature extraction, we propose an adaptive domain-invariant feature extracting module. Meanwhile, after taking inspiration from recent innovative meta-learning, we present a dynamic-β MAML algorithm to generate a density map in unseen novel scenes and render the density estimation model more universal. Finally, we use a counting map refiner to optimize the coarse density map transformation into a fine density map and then regress the crowd number. Extensive experiments show that our proposed domain adaptation- and model-generalization methods can effectively suppress domain gaps and produce elaborate density maps in cross-domain crowd-counting scenarios. We demonstrate that the proposals in our paper outperform current state-of-the-art techniques.

scholarly journals A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3382
Author(s):  
Zhongwei Zhang ◽  
Mingyu Shao ◽  
Liping Wang ◽  
Sujuan Shao ◽  
Chicheng Ma
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Class Imbalance ◽  
Rolling Bearing ◽  
Industrial Applications ◽  
Mechanical Equipment ◽  
Reliable Operation ◽  
Cross Domain ◽  
Geometric Difference ◽  
Model Generalization

As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The ℓ2-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.

Joint adaptive transfer learning network for cross-domain fault diagnosis based on multi-layer feature fusion

2021 ◽  
Author(s):  
Yimin Jiang ◽  
Tangbin Xia ◽  
Dong Wang ◽  
Kaigan Zhang ◽  
Lifeng Xi
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Feature Fusion ◽  
Learning Network ◽  
Cross Domain

scholarly journals Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation

2020 ◽  
pp. 1-1
Author(s):  
Jiahua Dong ◽  
Yang Cong ◽  
Gan Sun ◽  
Yunsheng Yang ◽  
Xiaowei Xu ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Cross Domain ◽  
Weakly Supervised

Correction to: AugGAN: Cross Domain Adaptation with GAN-Based Data Augmentation

2018 ◽  
pp. E1-E1 ◽  
Author(s):  
Sheng-Wei Huang ◽  
Che-Tsung Lin ◽  
Shu-Ping Chen ◽  
Yen-Yi Wu ◽  
Po-Hao Hsu ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Domain Adaptation ◽  
Cross Domain
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