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 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.

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

Low-Resolution Face Recognition with Single Sample per Person via Domain Adaptation

2019 ◽  
Vol 33 (05) ◽  
pp. 1956005 ◽  
Author(s):  
Yongjie Chu ◽  
Yong Zhao ◽  
Touqeer Ahmad ◽  
Lindu Zhao
Keyword(s):  
Face Recognition ◽  
Domain Adaptation ◽  
Face Image ◽  
Single Sample ◽  
Low Resolution ◽  
Target Domain ◽  
Discriminative Ability ◽  
Surveillance Camera ◽  
Single Sample Per Person ◽  
Suspect Identification

Numerous low-resolution (LR) face images are captured by a growing number of surveillance cameras nowadays. In some particular applications, such as suspect identification, it is required to recognize an LR face image captured by the surveillance camera using only one high-resolution (HR) profile face image on the ID card. This leads to LR face recognition with single sample per person (SSPP), which is more challenging than conventional LR face recognition or SSPP face recognition. To address this tough problem, we propose a Boosted Coupled Marginal Fisher Analysis (CMFA) approach, which unites domain adaptation and coupled mappings. An auxiliary database containing multiple HR and LR samples is introduced to explore more discriminative information, and locality preserving domain adaption (LPDA) is designed to realize good domain adaptation between SSPP training set (target domain) and auxiliary database (source domain). We perform LPDA on HR and LR images in both domains, then in the domain adaptation space we apply CMFA to learn the discriminative coupled mappings for classification. The learned coupled mappings embed knowledge from the auxiliary dataset, thus their discriminative ability is superior. We extensively evaluate the proposed method on FERET, LFW and SCface database, the promising results demonstrate its effectiveness on LR face recognition with SSPP.

ITERATIVE SELF-LABELING DOMAIN ADAPTATION FOR LINEAR STRUCTURED IMAGE CLASSIFICATION

2013 ◽  
Vol 22 (05) ◽  
pp. 1360005 ◽  
Author(s):  
AMAURY HABRARD ◽  
JEAN-PHILIPPE PEYRACHE ◽  
MARC SEBBAN
Keyword(s):  
Image Classification ◽  
Domain Adaptation ◽  
Research Area ◽  
Target Domain ◽  
Sparse Models ◽  
Similarity Functions ◽  
Generalization Bounds ◽  
Source Data ◽  
New Research ◽  
Target Data

A strong assumption to derive generalization guarantees in the standard PAC framework is that training (or source) data and test (or target) data are drawn according to the same distribution. Because of the presence of possibly outdated data in the training set, or the use of biased collections, this assumption is often violated in real-world applications leading to different source and target distributions. To go around this problem, a new research area known as Domain Adaptation (DA) has recently been introduced giving rise to many adaptation algorithms and theoretical results in the form of generalization bounds. This paper deals with self-labeling DA whose goal is to iteratively incorporate semi-labeled target data in the learning set to progressively adapt the classifier from the source to the target domain. The contribution of this work is three-fold: First, we provide the minimum and necessary theoretical conditions for a self-labeling DA algorithm to perform an actual domain adaptation. Second, following these theoretical recommendations, we design a new iterative DA algorithm, called GESIDA, able to deal with structured data. This algorithm makes use of the new theory of learning with (ε,γ,τ)-good similarity functions introduced by Balcan et al., which does not require the use of a valid kernel to learn well and allows us to induce sparse models. Finally, we apply our algorithm on a structured image classification task and show that self-labeling domain adaptation is a new original way to deal with scaling and rotation problems.

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 Synthetic Source Universal Domain Adaptation through Contrastive Learning

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7539
Author(s):  
Jungchan Cho
Keyword(s):  
Domain Adaptation ◽  
Synthetic Data ◽  
Real Data ◽  
Target Domain ◽  
Source Domain ◽  
Universal Domain ◽  
Model Training ◽  
Using Data ◽  
Imaging Sensors ◽  
Target Data

Universal domain adaptation (UDA) is a crucial research topic for efficient deep learning model training using data from various imaging sensors. However, its development is affected by unlabeled target data. Moreover, the nonexistence of prior knowledge of the source and target domain makes it more challenging for UDA to train models. I hypothesize that the degradation of trained models in the target domain is caused by the lack of direct training loss to improve the discriminative power of the target domain data. As a result, the target data adapted to the source representations is biased toward the source domain. I found that the degradation was more pronounced when I used synthetic data for the source domain and real data for the target domain. In this paper, I propose a UDA method with target domain contrastive learning. The proposed method enables models to leverage synthetic data for the source domain and train the discriminativeness of target features in an unsupervised manner. In addition, the target domain feature extraction network is shared with the source domain classification task, preventing unnecessary computational growth. Extensive experimental results on VisDa-2017 and MNIST to SVHN demonstrated that the proposed method significantly outperforms the baseline by 2.7% and 5.1%, respectively.

scholarly journals Domain Adaptation for Semantic Parsing

2020 ◽  
Author(s):  
Zechang Li ◽  
Yuxuan Lai ◽  
Yansong Feng ◽  
Dongyan Zhao
Keyword(s):  
Domain Adaptation ◽  
Specific Information ◽  
Semantic Parsing ◽  
Target Domain ◽  
Source Domain ◽  
Domain Specific ◽  
The Difference ◽  
Two Stages ◽  
Coarse To Fine ◽  
Target Data

Recently, semantic parsing has attracted much attention in the community. Although many neural modeling efforts have greatly improved the performance, it still suffers from the data scarcity issue. In this paper, we propose a novel semantic parser for domain adaptation, where we have much fewer annotated data in the target domain compared to the source domain. Our semantic parser benefits from a two-stage coarse-to-fine framework, thus can provide different and accurate treatments for the two stages, i.e., focusing on domain invariant and domain specific information, respectively. In the coarse stage, our novel domain discrimination component and domain relevance attention encourage the model to learn transferable domain general structures. In the fine stage, the model is guided to concentrate on domain related details. Experiments on a benchmark dataset show that our method consistently outperforms several popular domain adaptation strategies. Additionally, we show that our model can well exploit limited target data to capture the difference between the source and target domain, even when the target domain has far fewer training instances.

scholarly journals Unsupervised multi-source domain adaptation with no observable source data

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253415
Author(s):  
Hyunsik Jeon ◽  
Seongmin Lee ◽  
U Kang
Keyword(s):  
Real World ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Multiple Source ◽  
Multiple Sources ◽  
Target Domain ◽  
Source Domain ◽  
Source Data ◽  
Real World Datasets ◽  
Target Data

Given trained models from multiple source domains, how can we predict the labels of unlabeled data in a target domain? Unsupervised multi-source domain adaptation (UMDA) aims for predicting the labels of unlabeled target data by transferring the knowledge of multiple source domains. UMDA is a crucial problem in many real-world scenarios where no labeled target data are available. Previous approaches in UMDA assume that data are observable over all domains. However, source data are not easily accessible due to privacy or confidentiality issues in a lot of practical scenarios, although classifiers learned in source domains are readily available. In this work, we target data-free UMDA where source data are not observable at all, a novel problem that has not been studied before despite being very realistic and crucial. To solve data-free UMDA, we propose DEMS (Data-free Exploitation of Multiple Sources), a novel architecture that adapts target data to source domains without exploiting any source data, and estimates the target labels by exploiting pre-trained source classifiers. Extensive experiments for data-free UMDA on real-world datasets show that DEMS provides the state-of-the-art accuracy which is up to 27.5% point higher than that of the best baseline.

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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