scholarly journals Generalization Bounds Derived IPM-Based Regularization for Domain Adaptation

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Juan Meng ◽  
Guyu Hu ◽  
Dong Li ◽  
Yanyan Zhang ◽  
Zhisong Pan
Keyword(s):  
Domain Adaptation ◽  
Empirical Studies ◽  
Synthetic Data ◽  
Risk Minimization ◽  
Target Domain ◽  
Real World Data ◽  
Source Domain ◽  
Generalization Bounds ◽  
Empirical Risk ◽  
Testing Error

Domain adaptation has received much attention as a major form of transfer learning. One issue that should be considered in domain adaptation is the gap between source domain and target domain. In order to improve the generalization ability of domain adaption methods, we proposed a framework for domain adaptation combining source and target data, with a new regularizer which takes generalization bounds into account. This regularization term considers integral probability metric (IPM) as the distance between the source domain and the target domain and thus can bound up the testing error of an existing predictor from the formula. Since the computation of IPM only involves two distributions, this generalization term is independent with specific classifiers. With popular learning models, the empirical risk minimization is expressed as a general convex optimization problem and thus can be solved effectively by existing tools. Empirical studies on synthetic data for regression and real-world data for classification show the effectiveness of this method.

scholarly journals Latent Semantics Encoding for Label Distribution Learning

2019 ◽  
Author(s):  
Suping Xu ◽  
Lin Shang ◽  
Furao Shen
Keyword(s):  
Empirical Studies ◽  
Semantic Space ◽  
Learning Ability ◽  
Semantic Features ◽  
Risk Minimization ◽  
Negative Effects ◽  
Real World Data ◽  
Empirical Risk ◽  
Label Distribution Learning ◽  
Label Distribution

Label distribution learning (LDL) is a newly arisen learning paradigm to deal with label ambiguity problems, which can explore the relative importance of different labels in the description of a particular instance. Although some existing LDL algorithms have achieved better effectiveness in real applications, most of them typically emphasize on improving the learning ability by manipulating the label space, while ignoring the fact that irrelevant and redundant features exist in most practical classification learning tasks, which increase not only storage requirements but also computational overheads. Furthermore, noises in data acquisition will bring negative effects on the generalization performance of LDL algorithms. In this paper, we propose a novel algorithm, i.e., Latent Semantics Encoding for Label Distribution Learning (LSE-LDL), which learns the label distribution and implements feature selection simultaneously under the guidance of latent semantics. Specifically, to alleviate noise disturbances, we seek and encode discriminative original physical/chemical features into advanced latent semantic features, and then construct a mapping from the encoded semantic space to the label space via empirical risk minimization. Empirical studies on 15 real-world data sets validate the effectiveness of the proposed algorithm.

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.

A Classification Learning Research based on Discriminative Knowledge-Leverage Transfer

2018 ◽  
Vol 9 (4) ◽  
pp. 52-68 ◽  
Author(s):  
Ding Xiong ◽  
Lu Yan
Keyword(s):  
Transfer Learning ◽  
Data Sets ◽  
Empirical Risk Minimization ◽  
Risk Minimization ◽  
Target Domain ◽  
Source Domain ◽  
The Real ◽  
Empirical Risk ◽  
Real Scene ◽  
Source Data

Current transfer learning models study the source data for future target inferences within a major view, the whole source data should be used to explore the shared knowledge structure. However, human resources are constrained, the source domain data is collected as a whole in the real scene. However, this is not realistic, this data is associated with the target domain. A generalized empirical risk minimization model (GERM) is proposed in this article with discriminative knowledge-leverage (KL). The empirical risk minimization (ERM) principle is extended to the transfer learning setting. The theoretical upper bound of generalized ERM (GERM) is given for the practical discriminative transfer learning. The subset of the source domain data can be automatically selected in the model, and the source domain data is associated with the target domain. It can solve with only some knowledge of the source domain being available, thus it can avoid the negative transfer effect which is caused by the whole source domain dataset in the real scene. Simulation results show that the proposed algorithm is better than the traditional transfer learning algorithm in simulation data sets and real data sets.

Correlation alignment with attention mechanism for unsupervised domain adaptation

2021 ◽  
pp. 1-7
Author(s):  
Rong Chen ◽  
Chongguang Ren
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Transfer Process ◽  
Negative Transfer ◽  
Domain Adaptation ◽  
Attention Mechanism ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Unsupervised Domain Adaptation

Domain adaptation aims to solve the problems of lacking labels. Most existing works of domain adaptation mainly focus on aligning the feature distributions between the source and target domain. However, in the field of Natural Language Processing, some of the words in different domains convey different sentiment. Thus not all features of the source domain should be transferred, and it would cause negative transfer when aligning the untransferable features. To address this issue, we propose a Correlation Alignment with Attention mechanism for unsupervised Domain Adaptation (CAADA) model. In the model, an attention mechanism is introduced into the transfer process for domain adaptation, which can capture the positively transferable features in source and target domain. Moreover, the CORrelation ALignment (CORAL) loss is utilized to minimize the domain discrepancy by aligning the second-order statistics of the positively transferable features extracted by the attention mechanism. Extensive experiments on the Amazon review dataset demonstrate the effectiveness of CAADA method.

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 Financial Thought Experiment: A GAN-based Approach to Vast Robust Portfolio Selection

2020 ◽  
Author(s):  
Chi Seng Pun ◽  
Lei Wang ◽  
Hoi Ying Wong
Keyword(s):  
Stock Market ◽  
Thought Experiment ◽  
Empirical Studies ◽  
Synthetic Data ◽  
Risk Minimization ◽  
Portfolio Risk ◽  
Generative Adversarial Network ◽  
Day Trading ◽  
Adversarial Network ◽  
Robust Portfolio

Modern day trading practice resembles a thought experiment, where investors imagine various possibilities of future stock market and invest accordingly. Generative adversarial network (GAN) is highly relevant to this trading practice in two ways. First, GAN generates synthetic data by a neural network that is technically indistinguishable from the reality, which guarantees the reasonableness of the experiment. Second, GAN generates multitudes of fake data, which implements half of the experiment. In this paper, we present a new architecture of GAN and adapt it to portfolio risk minimization problem by adding a regression network to GAN (implementing the second half of the experiment). The new architecture is termed GANr. Battling against two distinctive networks: discriminator and regressor, GANr's generator aims to simulate a stock market that is close to the reality while allow for all possible scenarios. The resulting portfolio resembles a robust portfolio with data-driven ambiguity. Our empirical studies show that GANr portfolio is more resilient to bleak financial scenarios than CLSGAN and LASSO portfolios.

scholarly journals Joint Partial Optimal Transport for Open Set Domain Adaptation

2020 ◽  
Author(s):  
Renjun Xu ◽  
Pelen Liu ◽  
Yin Zhang ◽  
Fang Cai ◽  
Jindong Wang ◽  
...  
Keyword(s):  
Optimal Transport ◽  
Transport Model ◽  
Domain Adaptation ◽  
General Setting ◽  
Feature Space ◽  
Set Domain ◽  
Target Domain ◽  
Source Domain ◽  
Open Set ◽  
Proposed Model

Domain adaptation (DA) has achieved a resounding success to learn a good classifier by leveraging labeled data from a source domain to adapt to an unlabeled target domain. However, in a general setting when the target domain contains classes that are never observed in the source domain, namely in Open Set Domain Adaptation (OSDA), existing DA methods failed to work because of the interference of the extra unknown classes. This is a much more challenging problem, since it can easily result in negative transfer due to the mismatch between the unknown and known classes. Existing researches are susceptible to misclassification when target domain unknown samples in the feature space distributed near the decision boundary learned from the labeled source domain. To overcome this, we propose Joint Partial Optimal Transport (JPOT), fully utilizing information of not only the labeled source domain but also the discriminative representation of unknown class in the target domain. The proposed joint discriminative prototypical compactness loss can not only achieve intra-class compactness and inter-class separability, but also estimate the mean and variance of the unknown class through backpropagation, which remains intractable for previous methods due to the blindness about the structure of the unknown classes. To our best knowledge, this is the first optimal transport model for OSDA. Extensive experiments demonstrate that our proposed model can significantly boost the performance of open set domain adaptation on standard DA datasets.

scholarly journals Unsupervised Domain Adaptation by Statistics Alignment for Deep Sleep Staging Networks

2021 ◽  
Author(s):  
Jiahao Fan ◽  
Hangyu Zhu ◽  
Xinyu Jiang ◽  
Long Meng ◽  
Cong Fu ◽  
...  
Keyword(s):  
Large Scale ◽  
Domain Adaptation ◽  
Source Model ◽  
Deep Sleep ◽  
Sleep Staging ◽  
Target Domain ◽  
Source Domain ◽  
Unsupervised Domain Adaptation ◽  
Generalization Problem ◽  
Source Models

Deep sleep staging networks have reached top performance on large-scale datasets. However, these models perform poorer when training and testing on small sleep cohorts due to data inefficiency. Transferring well-trained models from large-scale datasets (source domain) to small sleep cohorts (target domain) is a promising solution but still remains challenging due to the domain-shift issue. In this work, an unsupervised domain adaptation approach, domain statistics alignment (DSA), is developed to bridge the gap between the data distribution of source and target domains. DSA adapts the source models on the target domain by modulating the domain-specific statistics of deep features stored in the Batch Normalization (BN) layers. Furthermore, we have extended DSA by introducing cross-domain statistics in each BN layer to perform DSA adaptively (AdaDSA). The proposed methods merely need the well-trained source model without access to the source data, which may be proprietary and inaccessible. DSA and AdaDSA are universally applicable to various deep sleep staging networks that have BN layers. We have validated the proposed methods by extensive experiments on two state-of-the-art deep sleep staging networks, DeepSleepNet+ and U-time. The performance was evaluated by conducting various transfer tasks on six sleep databases, including two large-scale databases, MASS and SHHS, as the source domain, four small sleep databases as the target domain. Thereinto, clinical sleep records acquired in Huashan Hospital, Shanghai, were used. The results show that both DSA and AdaDSA could significantly improve the performance of source models on target domains, providing novel insights into the domain generalization problem in sleep staging tasks.<br>

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 Optimal Transport with Dimensionality Reduction for Domain Adaptation

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1994
Author(s):  
Ping Li ◽  
Zhiwei Ni ◽  
Xuhui Zhu ◽  
Juan Song ◽  
Wenying Wu
Keyword(s):  
Dimensionality Reduction ◽  
Optimal Transport ◽  
Domain Adaptation ◽  
Wasserstein Distance ◽  
Local Information ◽  
Target Domain ◽  
Source Domain ◽  
Second Stage ◽  
Cross Domain ◽  
Feature Based

Domain adaptation manages to learn a robust classifier for target domain, using the source domain, but they often follow different distributions. To bridge distribution shift between the two domains, most of previous works aim to align their feature distributions through feature transformation, of which optimal transport for domain adaptation has attract researchers’ interest, as it can exploit the local information of the two domains in the process of mapping the source instances to the target ones by minimizing Wasserstein distance between their feature distributions. However, it may weaken the feature discriminability of source domain, thus degrade domain adaptation performance. To address this problem, this paper proposes a two-stage feature-based adaptation approach, referred to as optimal transport with dimensionality reduction (OTDR). In the first stage, we apply the dimensionality reduction with intradomain variant maximization but source intraclass compactness minimization, to separate data samples as much as possible and enhance the feature discriminability of the source domain. In the second stage, we leverage optimal transport-based technique to preserve the local information of the two domains. Notably, the desirable properties in the first stage can mitigate the degradation of feature discriminability of the source domain in the second stage. Extensive experiments on several cross-domain image datasets validate that OTDR is superior to its competitors in classification accuracy.

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