An Adversarial Domain Adaptation Network For Cross-Domain Fine-Grained Recognition

Uncertainty is an important linguistic phenomenon that is relevant in various Natural Language Processing applications, in diverse genres from medical to community generated, newswire or scientific discourse, and domains from science to humanities. The semantic uncertainty of a proposition can be identified in most cases by using a finite dictionary (i.e., lexical cues) and the key steps of uncertainty detection in an application include the steps of locating the (genre- and domain-specific) lexical cues, disambiguating them, and linking them with the units of interest for the particular application (e.g., identified events in information extraction). In this study, we focus on the genre and domain differences of the context-dependent semantic uncertainty cue recognition task. We introduce a unified subcategorization of semantic uncertainty as different domain applications can apply different uncertainty categories. Based on this categorization, we normalized the annotation of three corpora and present results with a state-of-the-art uncertainty cue recognition model for four fine-grained categories of semantic uncertainty. Our results reveal the domain and genre dependence of the problem; nevertheless, we also show that even a distant source domain data set can contribute to the recognition and disambiguation of uncertainty cues, efficiently reducing the annotation costs needed to cover a new domain. Thus, the unified subcategorization and domain adaptation for training the models offer an efficient solution for cross-domain and cross-genre semantic uncertainty recognition.

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Cross-Domain End-To-End Aspect-Based Sentiment Analysis with Domain-Dependent Embeddings

Complexity ◽

10.1155/2021/5529312 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yingjie Tian ◽

Linrui Yang ◽

Yunchuan Sun ◽

Dalian. Liu

Keyword(s):

Sentiment Analysis ◽

Opinion Mining ◽

Learning Strategy ◽

Domain Adaptation ◽

Online Reviews ◽

Domain Model ◽

Fine Grained ◽

Cross Domain ◽

End To End ◽

Public Datasets

With the development of sentiment analysis, studies have been gradually classified based on different researched candidates. Among them, aspect-based sentiment analysis plays an important role in subtle opinion mining for online reviews. It used to be treated as a group of pipeline tasks but has been proved to be analysed well in an end-to-end model recently. Due to less labelled resources, the need for cross-domain aspect-based sentiment analysis has started to get attention. However, challenges exist when seeking domain-invariant features and keeping domain-dependent features to achieve domain adaptation within a fine-grained task. This paper utilizes the domain-dependent embeddings and designs the model CD-E2EABSA to achieve cross-domain aspect-based sentiment analysis in an end-to-end fashion. The proposed model utilizes the domain-dependent embeddings with a multitask learning strategy to capture both domain-invariant and domain-dependent knowledge. Various experiments are conducted and show the effectiveness of all components on two public datasets. Also, it is also proved that as a cross-domain model, CD-E2EABSA can perform better than most of the in-domain ABSA methods.

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Robust adversarial discriminative domain adaptation for real-world cross-domain visual recognition

Neurocomputing ◽

10.1016/j.neucom.2020.12.046 ◽

2021 ◽

Author(s):

Jianfei Yang ◽

Han Zou ◽

Yuxun Zhou ◽

Lihua Xie

Keyword(s):

Real World ◽

Visual Recognition ◽

Domain Adaptation ◽

Cross Domain

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

Sensors ◽

10.3390/s21103382 ◽

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.

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Deep transfer learning mechanism for fine-grained cross-domain sentiment classification

Connection Science ◽

10.1080/09540091.2021.1912711 ◽

2021 ◽

pp. 1-18

Author(s):

Zixuan Cao ◽

Yongmei Zhou ◽

Aimin Yang ◽

Sancheng Peng

Keyword(s):

Transfer Learning ◽

Sentiment Classification ◽

Learning Mechanism ◽

Fine Grained ◽

Cross Domain

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Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors

Reliability Engineering & System Safety ◽

10.1016/j.ress.2021.107946 ◽

2021 ◽

pp. 107946

Author(s):

Jichao Zhuang ◽

Minping Jia ◽

Yifei Ding ◽

Peng Ding

Keyword(s):

Domain Adaptation ◽

Remaining Useful Life ◽

Life Estimation ◽

Cross Domain ◽

Useful Life

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DAFI

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3494954 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1-21

Author(s):

Hang Li ◽

Xi Chen ◽

Ju Wang ◽

Di Wu ◽

Xue Liu

Keyword(s):

Indoor Localization ◽

Domain Adaptation ◽

Target Domain ◽

Localization Accuracy ◽

Source Domain ◽

Fine Grained ◽

Time Signals ◽

Environmental Variations ◽

Device Free ◽

Localization Approach

WiFi-based Device-free Passive (DfP) indoor localization systems liberate their users from carrying dedicated sensors or smartphones, and thus provide a non-intrusive and pleasant experience. Although existing fingerprint-based systems achieve sub-meter-level localization accuracy by training location classifiers/regressors on WiFi signal fingerprints, they are usually vulnerable to small variations in an environment. A daily change, e.g., displacement of a chair, may cause a big inconsistency between the recorded fingerprints and the real-time signals, leading to significant localization errors. In this paper, we introduce a Domain Adaptation WiFi (DAFI) localization approach to address the problem. DAFI formulates this fingerprint inconsistency issue as a domain adaptation problem, where the original environment is the source domain and the changed environment is the target domain. Directly applying existing domain adaptation methods to our specific problem is challenging, since it is generally hard to distinguish the variations in the different WiFi domains (i.e., signal changes caused by different environmental variations). DAFI embraces the following techniques to tackle this challenge. 1) DAFI aligns both marginal and conditional distributions of features in different domains. 2) Inside the target domain, DAFI squeezes the marginal distribution of every class to be more concentrated at its center. 3) Between two domains, DAFI conducts fine-grained alignment by forcing every target-domain class to better align with its source-domain counterpart. By doing these, DAFI outperforms the state of the art by up to 14.2% in real-world experiments.

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