scholarly journals Transferable Interactive Memory Network for Domain Adaptation in Fine-Grained Opinion Extraction

2019 ◽  
Vol 33 ◽  
pp. 7192-7199 ◽  
Author(s):  
Wenya Wang ◽  
Sinno Jialin Pan
Keyword(s):  
Opinion Mining ◽  
Domain Adaptation ◽  
Global Memory ◽  
Target Space ◽  
Target Domain ◽  
Fine Grained ◽  
Opinion Extraction ◽  
Benchmark Datasets ◽  
Memory Network ◽  
Syntactic Relations

In fine-grained opinion mining, aspect and opinion terms extraction has become a fundamental task that provides key information for user-generated texts. Despite its importance, a lack of annotated resources in many domains impede the ability to train a precise model. Very few attempts have applied unsupervised domain adaptation methods to transfer fine-grained knowledge (in the word level) from some labeled source domain(s) to any unlabeled target domain. Existing methods depend on the construction of “pivot” knowledge, e.g., common opinion terms or syntactic relations between aspect and opinion words. In this work, we propose an interactive memory network that consists of local and global memory units. The model could exploit both local and global memory interactions to capture intra-correlations among aspect words or opinion words themselves, as well as the interconnections between aspect and opinion words. The source space and the target space are aligned through these domaininvariant interactions by incorporating an auxiliary task and domain adversarial networks. The proposed model does not require any external resources and demonstrates promising results on 3 benchmark datasets.

DAFI

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.

scholarly journals TSAL: Two steps Adversarial learning based domain adaptation

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Haidi Hasan Badr ◽  
Nayer Mahmoud Wanas ◽  
Magda Fayek
Keyword(s):  
Network Architecture ◽  
Domain Adaptation ◽  
Data Availability ◽  
Fine Tuning ◽  
Target Domain ◽  
Adversarial Learning ◽  
Prior Art ◽  
Real World Problem ◽  
Wide Range ◽  
Benchmark Datasets

Since labeled data availability differs greatly across domains, Domain Adaptation focuses on learning in new and unfamiliar domains by reducing distribution divergence. Recent research suggests that the adversarial learning approach could be a promising way to achieve the domain adaptation objective. Adversarial learning is a strategy for learning domain-transferable features in robust deep networks. This paper introduces the TSAL paradigm, a two-step adversarial learning framework. It addresses the real-world problem of text classification, where source domain(s) has labeled data but target domain (s) has only unlabeled data. TSAL utilizes joint adversarial learning with class information and domain alignment deep network architecture to learn both domain-invariant and domain-specific features extractors. It consists of two training steps that are similar to the paradigm, in which pre-trained model weights are used as initialization for training with new data. TSAL’s two training phases, however, are based on the same data, not different data, as is the case with fine-tuning. Furthermore, TSAL only uses the learned domain-invariant feature extractor from the first training as an initialization for its peer in subsequent training. By doubling the training, TSAL can emphasize the leverage of the small unlabeled target domain and learn effectively what to share between various domains. A detailed analysis of many benchmark datasets reveals that our model consistently outperforms the prior art across a wide range of dataset distributions.

scholarly journals A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 424
Author(s):  
Sixiang Jia ◽  
Jinrui Wang ◽  
Xiao Zhang ◽  
Baokun Han
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Local Maximum ◽  
Fault Classification ◽  
Target Domain ◽  
Local Data ◽  
Source Domain ◽  
Fine Grained ◽  
Realistic Situation ◽  
Partial Transfer

Domain adaptation-based models for fault classification under variable working conditions have become a research focus in recent years. Previous domain adaptation approaches generally assume identical label spaces in the source and target domains, however, such an assumption may be no longer legitimate in a more realistic situation that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of fault classes. To address the above deficiencies, we propose a partial transfer fault diagnosis model based on a weighted subdomain adaptation network (WSAN) in this paper. Our method pays more attention to the local data distribution while aligning the global distribution. An auxiliary classifier is introduced to obtain the class-level weights of the source samples, so the network can avoid negative transfer caused by unique fault classes in the source domain. Furthermore, a weighted local maximum mean discrepancy (WLMMD) is proposed to capture the fine-grained transferable information and obtain sample-level weights. Finally, relevant distributions of domain-specific layer activations across different domains are aligned. Experimental results show that our method could assign appropriate weights to each source sample and realize efficient partial transfer fault diagnosis.

scholarly journals Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation

2019 ◽  
Vol 33 ◽  
pp. 5581-5588 ◽  
Author(s):  
Yonghao Xu ◽  
Bo Du ◽  
Lefei Zhang ◽  
Qian Zhang ◽  
Guoli Wang ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Great Success ◽  
Learning Models ◽  
Target Domain ◽  
Attention Networks ◽  
Source Domain ◽  
Benchmark Datasets ◽  
Different Levels

Recent years have witnessed the great success of deep learning models in semantic segmentation. Nevertheless, these models may not generalize well to unseen image domains due to the phenomenon of domain shift. Since pixel-level annotations are laborious to collect, developing algorithms which can adapt labeled data from source domain to target domain is of great significance. To this end, we propose self-ensembling attention networks to reduce the domain gap between different datasets. To the best of our knowledge, the proposed method is the first attempt to introduce selfensembling model to domain adaptation for semantic segmentation, which provides a different view on how to learn domain-invariant features. Besides, since different regions in the image usually correspond to different levels of domain gap, we introduce the attention mechanism into the proposed framework to generate attention-aware features, which are further utilized to guide the calculation of consistency loss in the target domain. Experiments on two benchmark datasets demonstrate that the proposed framework can yield competitive performance compared with the state of the art methods.

scholarly journals Multi-Source Domain Adaptation for Visual Sentiment Classification

2020 ◽  
Vol 34 (03) ◽  
pp. 2661-2668
Author(s):  
Chuang Lin ◽  
Sicheng Zhao ◽  
Lei Meng ◽  
Tat-Seng Chua
Keyword(s):  
Domain Adaptation ◽  
Sentiment Classification ◽  
Similar Distribution ◽  
Single Source ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Source Domain ◽  
Adversarial Network ◽  
Latent Space ◽  
Benchmark Datasets

Existing domain adaptation methods on visual sentiment classification typically are investigated under the single-source scenario, where the knowledge learned from a source domain of sufficient labeled data is transferred to the target domain of loosely labeled or unlabeled data. However, in practice, data from a single source domain usually have a limited volume and can hardly cover the characteristics of the target domain. In this paper, we propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN), for visual sentiment classification. To handle data from multiple source domains, it learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. This is achieved via cycle consistent adversarial learning in an end-to-end manner. Extensive experiments conducted on four benchmark datasets demonstrate that MSGAN significantly outperforms the state-of-the-art MDA approaches for visual sentiment classification.

Knowledge Preserving and Distribution Alignment for Heterogeneous Domain Adaptation

2022 ◽  
Vol 40 (1) ◽  
pp. 1-29
Author(s):  
Hanrui Wu ◽  
Qingyao Wu ◽  
Michael K. Ng
Keyword(s):  
Domain Adaptation ◽  
Iteration Scheme ◽  
Target Space ◽  
Target Domain ◽  
Maximum Mean Discrepancy ◽  
Learning Tasks ◽  
Feature Spaces ◽  
Latent Space ◽  
Laplacian Graph ◽  
Novel Model

Domain adaptation aims at improving the performance of learning tasks in a target domain by leveraging the knowledge extracted from a source domain. To this end, one can perform knowledge transfer between these two domains. However, this problem becomes extremely challenging when the data of these two domains are characterized by different types of features, i.e., the feature spaces of the source and target domains are different, which is referred to as heterogeneous domain adaptation (HDA). To solve this problem, we propose a novel model called Knowledge Preserving and Distribution Alignment (KPDA), which learns an augmented target space by jointly minimizing information loss and maximizing domain distribution alignment. Specifically, we seek to discover a latent space, where the knowledge is preserved by exploiting the Laplacian graph terms and reconstruction regularizations. Moreover, we adopt the Maximum Mean Discrepancy to align the distributions of the source and target domains in the latent space. Mathematically, KPDA is formulated as a minimization problem with orthogonal constraints, which involves two projection variables. Then, we develop an algorithm based on the Gauss–Seidel iteration scheme and split the problem into two subproblems, which are solved by searching algorithms based on the Barzilai–Borwein (BB) stepsize. Promising results demonstrate the effectiveness of the proposed method.

scholarly journals Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation

2020 ◽  
Author(s):  
Pin Jiang ◽  
Aming Wu ◽  
Yahong Han ◽  
Yunfeng Shao ◽  
Meiyu Qi ◽  
...  
Keyword(s):  
Additional Data ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Powerful Method ◽  
Target Domain ◽  
Unsupervised Domain Adaptation ◽  
Benchmark Datasets ◽  
Adversarial Examples ◽  
Adversarial Training ◽  
Effective Use

Semi-supervised domain adaptation (SSDA) is a novel branch of machine learning that scarce labeled target examples are available, compared with unsupervised domain adaptation. To make effective use of these additional data so as to bridge the domain gap, one possible way is to generate adversarial examples, which are images with additional perturbations, between the two domains and fill the domain gap. Adversarial training has been proven to be a powerful method for this purpose. However, the traditional adversarial training adds noises in arbitrary directions, which is inefficient to migrate between domains, or generate directional noises from the source to target domain and reverse. In this work, we devise a general bidirectional adversarial training method and employ gradient to guide adversarial examples across the domain gap, i.e., the Adaptive Adversarial Training (AAT) for source to target domain and Entropy-penalized Virtual Adversarial Training (E-VAT) for target to source domain. Particularly, we devise a Bidirectional Adversarial Training (BiAT) network to perform diverse adversarial trainings jointly. We evaluate the effectiveness of BiAT on three benchmark datasets and experimental results demonstrate the proposed method achieves the state-of-the-art.

scholarly journals End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification

2017 ◽  
Author(s):  
Zheng Li ◽  
Yu Zhang ◽  
Ying Wei ◽  
Yuxiang Wu ◽  
Qiang Yang
Keyword(s):  
Deep Learning ◽  
Domain Adaptation ◽  
Sentiment Classification ◽  
Classification Error ◽  
Target Domain ◽  
Learning Methods ◽  
Cross Domain ◽  
Memory Network ◽  
End To End ◽  
Two Parameter

Domain adaptation tasks such as cross-domain sentiment classification have raised much attention in recent years. Due to the domain discrepancy, a sentiment classifier trained in a source domain may not work well when directly applied to a target domain. Traditional methods need to manually select pivots, which behave in the same way for discriminative learning in both domains. Recently, deep learning methods have been proposed to learn a representation shared by domains. However, they lack the interpretability to directly identify the pivots. To address the problem, we introduce an end-to-end Adversarial Memory Network (AMN) for cross-domain sentiment classification. Unlike existing methods, our approach can automatically capture the pivots using an attention mechanism. Our framework consists of two parameter-shared memory networks: one is for sentiment classification and the other is for domain classification. The two networks are jointly trained so that the selected features minimize the sentiment classification error and at the same time make the domain classifier indiscriminative between the representations from the source or target domains. Moreover, unlike deep learning methods that cannot tell us which words are the pivots, our approach can offer a direct visualization of them. Experiments on the Amazon review dataset demonstrate that our approach can significantly outperform state-of-the-art methods.

scholarly journals Enhancement of Target-Oriented Opinion Words Extraction with Multiview-Trained Machine Reading Comprehension Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jingyuan Zhang ◽  
Zequn Zhang ◽  
Zhi Guo ◽  
Li Jin ◽  
Kang Liu ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Question Answering ◽  
Opinion Mining ◽  
Common Knowledge ◽  
Multiple Perspectives ◽  
Fine Grained ◽  
Proposed Model ◽  
Meta Learning ◽  
Benchmark Datasets ◽  
Machine Reading

Target-oriented opinion words extraction (TOWE) seeks to identify opinion expressions oriented to a specific target, and it is a crucial step toward fine-grained opinion mining. Recent neural networks have achieved significant success in this task by building target-aware representations. However, there are still two limitations of these methods that hinder the progress of TOWE. Mainstream approaches typically utilize position indicators to mark the given target, which is a naive strategy and lacks task-specific semantic meaning. Meanwhile, the annotated target-opinion pairs contain rich latent structural knowledge from multiple perspectives, but existing methods only exploit the TOWE view. To tackle these issues, we formulate the TOWE task as a question answering (QA) problem and leverage a machine reading comprehension (MRC) model trained with a multiview paradigm to extract targeted opinions. Specifically, we introduce a template-based pseudo-question generation method and utilize deep attention interaction to build target-aware context representations and extract related opinion words. To take advantage of latent structural correlations, we further cast the opinion-target structure into three distinct yet correlated views and leverage meta-learning to aggregate common knowledge among them to enhance the TOWE task. We evaluate the proposed model on four benchmark datasets, and our method achieves new state-of-the-art results. Extensional experiments have shown that the pipeline method with our approach could surpass existing opinion pair extraction models, including joint methods that are usually believed to work better.

scholarly journals Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation

2021 ◽  
Author(s):  
Zhen Qiu ◽  
Yifan Zhang ◽  
Hongbin Lin ◽  
Shuaicheng Niu ◽  
Yanxia Liu ◽  
...  
Keyword(s):  
Data Privacy ◽  
Domain Adaptation ◽  
Adaptation Strategy ◽  
Source Model ◽  
Target Domain ◽  
Benchmark Datasets ◽  
Classification Boundary ◽  
Hidden Knowledge ◽  
Two Stages ◽  
Target Data

We study a practical domain adaptation task, called source-free unsupervised domain adaptation (UDA) problem, in which we cannot access source domain data due to data privacy issues but only a pre-trained source model and unlabeled target data are available. This task, however, is very difficult due to one key challenge: the lack of source data and target domain labels makes model adaptation very challenging. To address this, we propose to mine the hidden knowledge in the source model and exploit it to generate source avatar prototypes (i.e. representative features for each source class) as well as target pseudo labels for domain alignment. To this end, we propose a Contrastive Prototype Generation and Adaptation (CPGA) method. Specifically, CPGA consists of two stages: (1) prototype generation: by exploring the classification boundary information of the source model, we train a prototype generator to generate avatar prototypes via contrastive learning. (2) prototype adaptation: based on the generated source prototypes and target pseudo labels, we develop a new robust contrastive prototype adaptation strategy to align each pseudo-labeled target data to the corresponding source prototypes. Extensive experiments on three UDA benchmark datasets demonstrate the effectiveness and superiority of the proposed method.

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