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 Learning Discriminative Correlation Subspace for Heterogeneous Domain Adaptation

2017 ◽  
Author(s):  
Yuguang Yan ◽  
Wen Li ◽  
Michael Ng ◽  
Mingkui Tan ◽  
Hanrui Wu ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Domain Adaptation ◽  
Data Sets ◽  
Target Domain ◽  
Real World Data ◽  
Discriminative Ability ◽  
Convex Optimization Problem ◽  
Alternating Direction ◽  
Feature Spaces ◽  
Target Data

Domain adaptation aims to reduce the effort on collecting and annotating target data by leveraging knowledge from a different source domain. The domain adaptation problem will become extremely challenging when the feature spaces of the source and target domains are different, which is also known as the heterogeneous domain adaptation (HDA) problem. In this paper, we propose a novel HDA method to find the optimal discriminative correlation subspace for the source and target data. The discriminative correlation subspace is inherited from the canonical correlation subspace between the source and target data, and is further optimized to maximize the discriminative ability for the target domain classifier. We formulate a joint objective in order to simultaneously learn the discriminative correlation subspace and the target domain classifier. We then apply an alternating direction method of multiplier (ADMM) algorithm to address the resulting non-convex optimization problem. Comprehensive experiments on two real-world data sets demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods.

scholarly journals Domain Adversarial Neural Networks for Large-Scale Land Cover Classification

2019 ◽  
Vol 11 (10) ◽  
pp. 1153 ◽  
Author(s):  
Mesay Belete Bejiga ◽  
Farid Melgani ◽  
Pietro Beraldini
Keyword(s):  
Neural Networks ◽  
Land Cover ◽  
Transfer Learning ◽  
Large Scale ◽  
Domain Adaptation ◽  
Land Cover Classification ◽  
Target Domain ◽  
Training Samples ◽  
Latent Space ◽  
Large Scale Land

Learning classification models require sufficiently labeled training samples, however, collecting labeled samples for every new problem is time-consuming and costly. An alternative approach is to transfer knowledge from one problem to another, which is called transfer learning. Domain adaptation (DA) is a type of transfer learning that aims to find a new latent space where the domain discrepancy between the source and the target domain is negligible. In this work, we propose an unsupervised DA technique called domain adversarial neural networks (DANNs), composed of a feature extractor, a class predictor, and domain classifier blocks, for large-scale land cover classification. Contrary to the traditional methods that perform representation and classifier learning in separate stages, DANNs combine them into a single stage, thereby learning a new representation of the input data that is both domain-invariant and discriminative. Once trained, the classifier of a DANN can be used to predict both source and target domain labels. Additionally, we also modify the domain classifier of a DANN to evaluate its suitability for multi-target domain adaptation problems. Experimental results obtained for both single and multiple target DA problems show that the proposed method provides a performance gain of up to 40%.

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.

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.

scholarly journals A COMPARISON OF TWO STRATEGIES FOR AVOIDING NEGATIVE TRANSFER IN DOMAIN ADAPTATION BASED ON LOGISTIC REGRESSION

2018 ◽  
Vol XLII-2 ◽  
pp. 845-852 ◽  
Author(s):  
A. Paul ◽  
K. Vogt ◽  
F. Rottensteiner ◽  
J. Ostermann ◽  
C. Heipke
Keyword(s):  
Negative Transfer ◽  
Domain Adaptation ◽  
Classification Performance ◽  
Target Domain ◽  
Maximum Mean Discrepancy ◽  
Source Domain ◽  
Training Samples ◽  
Benchmark Datasets ◽  
Consistent Performance ◽  
Class Labels

In this paper we deal with the problem of measuring the similarity between training and tests datasets in the context of transfer learning (TL) for image classification. TL tries to transfer knowledge from a source domain, where labelled training samples are abundant but the data may follow a different distribution, to a target domain, where labelled training samples are scarce or even unavailable, assuming that the domains are related. Thus, the requirements w.r.t. the availability of labelled training samples in the target domain are reduced. In particular, if no labelled target data are available, it is inherently difficult to find a robust measure of relatedness between the source and target domains. This is of crucial importance for the performance of TL, because the knowledge transfer between unrelated data may lead to negative transfer, i.e. to a decrease of classification performance after transfer. We address the problem of measuring the relatedness between source and target datasets and investigate three different strategies to predict and, consequently, to avoid negative transfer in this paper. The first strategy is based on circular validation. The second strategy relies on the Maximum Mean Discrepancy (MMD) similarity metric, whereas the third one is an extension of MMD which incorporates the knowledge about the class labels in the source domain. Our method is evaluated using two different benchmark datasets. The experiments highlight the strengths and weaknesses of the investigated methods. We also show that it is possible to reduce the amount of negative transfer using these strategies for a TL method and to generate a consistent performance improvement over the whole dataset.

scholarly journals General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer

2017 ◽  
Author(s):  
Yong Luo ◽  
Yonggang Wen ◽  
Tongliang Liu ◽  
Dacheng Tao
Keyword(s):  
Transfer Learning ◽  
Metric Learning ◽  
Learning Task ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Target Domain ◽  
Feature Representations ◽  
Transfer Distance ◽  
Learning Tasks ◽  
Feature Spaces

Transfer learning aims to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. Recently, transfer distance metric learning (TDML) has attracted lots of interests, but most of these methods assume that feature representations for the source and target learning tasks are the same. Hence, they are not suitable for the applications, in which the data are from heterogeneous domains (feature spaces, modalities and even semantics). Although some existing heterogeneous transfer learning (HTL) approaches is able to handle such domains, they lack flexibility in real-world applications, and the learned transformations are often restricted to be linear. We therefore develop a general and flexible heterogeneous TDML (HTDML) framework based on the knowledge fragment transfer strategy. In the proposed HTDML, any (linear or nonlinear) distance metric learning algorithms can be employed to learn the source metric beforehand. Then a set of knowledge fragments are extracted from the pre-learned source metric to help target metric learning. In addition, either linear or nonlinear distance metric can be learned for the target domain. Extensive experiments on both scene classification and object recognition demonstrate superiority of the proposed method.

scholarly journals Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation

2021 ◽  
Author(s):  
Qiao Liu ◽  
Hui Xue
Keyword(s):  
Time Series ◽  
Real World ◽  
Domain Adaptation ◽  
Kernel Learning ◽  
Target Domain ◽  
Maximum Mean Discrepancy ◽  
Local Statistics ◽  
Unsupervised Domain Adaptation ◽  
Non Local ◽  
Spectral Kernel

Unsupervised domain adaptation (UDA) has been received increasing attention since it does not require labels in target domain. Most existing UDA methods learn domain-invariant features by minimizing discrepancy distance computed by a certain metric between domains. However, these discrepancy-based methods cannot be robustly applied to unsupervised time series domain adaptation (UTSDA). That is because discrepancy metrics in these methods contain only low-order and local statistics, which have limited expression for time series distributions and therefore result in failure of domain matching. Actually, the real-world time series are always non-local distributions, i.e., with non-stationary and non-monotonic statistics. In this paper, we propose an Adversarial Spectral Kernel Matching (AdvSKM) method, where a hybrid spectral kernel network is specifically designed as inner kernel to reform the Maximum Mean Discrepancy (MMD) metric for UTSDA. The hybrid spectral kernel network can precisely characterize non-stationary and non-monotonic statistics in time series distributions. Embedding hybrid spectral kernel network to MMD not only guarantees precise discrepancy metric but also benefits domain matching. Besides, the differentiable architecture of the spectral kernel network enables adversarial kernel learning, which brings more discriminatory expression for discrepancy matching. The results of extensive experiments on several real-world UTSDA tasks verify the effectiveness of our proposed method.

scholarly journals Differentially Private Correlation Alignment for Domain Adaptation

2021 ◽  
Author(s):  
Kaizhong Jin ◽  
Xiang Cheng ◽  
Jiaxi Yang ◽  
Kaiyuan Shen
Keyword(s):  
Covariance Matrix ◽  
Domain Adaptation ◽  
Estimation Method ◽  
Training Data ◽  
Target Domain ◽  
Source Domain ◽  
Feature Spaces ◽  
Benchmark Datasets ◽  
Low Dimensional ◽  
Target Data

Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. As a simple and efficient method for domain adaptation, correlation alignment transforms the distribution of the source domain by utilizing the covariance matrix of the target domain, such that a model trained on the transformed source data can be applied to the target data. However, when source and target domains come from different institutes, exchanging information between the two domains might pose a potential privacy risk. In this paper, for the first time, we propose a differentially private correlation alignment approach for domain adaptation called PRIMA, which can provide privacy guarantees for both the source and target data. In PRIMA, to relieve the performance degradation caused by perturbing the covariance matrix in high dimensional setting, we present a random subspace ensemble based covariance estimation method which splits the feature spaces of source and target data into several low dimensional subspaces. Moreover, since perturbing the covariance matrix may destroy its positive semi-definiteness, we develop a shrinking based method for the recovery of positive semi-definiteness of the covariance matrix. Experimental results on standard benchmark datasets confirm the effectiveness of our approach.

scholarly journals Discriminative Sparse Filtering for Multi-Source Image Classification

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5868 ◽  
Author(s):  
Chao Han ◽  
Deyun Zhou ◽  
Zhen Yang ◽  
Yu Xie ◽  
Kai Zhang
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Source Image ◽  
Unified Framework ◽  
Target Domain ◽  
Maximum Mean Discrepancy ◽  
Source Domain ◽  
Cross Domain ◽  
Sparse Filtering ◽  
Novel Approach

Distribution mismatch caused by various resolutions, backgrounds, etc. can be easily found in multi-sensor systems. Domain adaptation attempts to reduce such domain discrepancy by means of different measurements, e.g., maximum mean discrepancy (MMD). Despite their success, such methods often fail to guarantee the separability of learned representation. To tackle this issue, we put forward a novel approach to jointly learn both domain-shared and discriminative representations. Specifically, we model the feature discrimination explicitly for two domains. Alternating discriminant optimization is proposed to obtain discriminative features with an l2 constraint in labeled source domain and sparse filtering is introduced to capture the intrinsic structures exists in the unlabeled target domain. Finally, they are integrated in a unified framework along with MMD to align domains. Extensive experiments compared with state-of-the-art methods verify the effectiveness of our method on cross-domain tasks.

scholarly journals Confidence Measures for Deep Learning in Domain Adaptation

2019 ◽  
Vol 9 (11) ◽  
pp. 2192 ◽  
Author(s):  
Simone Bonechi ◽  
Paolo Andreini ◽  
Monica Bianchini ◽  
Akshay Pai ◽  
Franco Scarselli
Keyword(s):  
Domain Adaptation ◽  
Medical Image Analysis ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Huge Amount ◽  
Confidence Measures ◽  
Adversarial Networks ◽  
Large Sets ◽  
Learning Tasks ◽  
Network Output

In recent years, Deep Neural Networks (DNNs) have led to impressive results in a wide variety of machine learning tasks, typically relying on the existence of a huge amount of supervised data. However, in many applications (e.g., bio–medical image analysis), gathering large sets of labeled data can be very difficult and costly. Unsupervised domain adaptation exploits data from a source domain, where annotations are available, to train a model able to generalize also to a target domain, where labels are unavailable. Recent research has shown that Generative Adversarial Networks (GANs) can be successfully employed for domain adaptation, although deciding when to stop learning is a major concern for GANs. In this work, we propose some confidence measures that can be used to early stop the GAN training, also showing how such measures can be employed to predict the reliability of the network output. The effectiveness of the proposed approach has been tested in two domain adaptation tasks, with very promising results.

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