scholarly journals Wasserstein Distance Learns Domain Invariant Feature Representations for Drift Compensation of E-Nose

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3703 ◽  
Author(s):  
Yang Tao ◽  
Chunyan Li ◽  
Zhifang Liang ◽  
Haocheng Yang ◽  
Juan Xu
Keyword(s):  
Feature Space ◽  
Representation Learning ◽  
Wasserstein Distance ◽  
Recognition Algorithm ◽  
Feature Representation ◽  
Target Domain ◽  
Feature Representations ◽  
Invariant Feature ◽  
Sensor Drift ◽  
Drift Compensation

Electronic nose (E-nose), a kind of instrument which combines with the gas sensor and the corresponding pattern recognition algorithm, is used to detect the type and concentration of gases. However, the sensor drift will occur in realistic application scenario of E-nose, which makes a variation of data distribution in feature space and causes a decrease in prediction accuracy. Therefore, studies on the drift compensation algorithms are receiving increasing attention in the field of the E-nose. In this paper, a novel method, namely Wasserstein Distance Learned Feature Representations (WDLFR), is put forward for drift compensation, which is based on the domain invariant feature representation learning. It regards a neural network as a domain discriminator to measure the empirical Wasserstein distance between the source domain (data without drift) and target domain (drift data). The WDLFR minimizes Wasserstein distance by optimizing the feature extractor in an adversarial manner. The Wasserstein distance for domain adaption has good gradient and generalization bound. Finally, the experiments are conducted on a real dataset of E-nose from the University of California, San Diego (UCSD). The experimental results demonstrate that the effectiveness of the proposed method outperforms all compared drift compensation methods, and the WDLFR succeeds in significantly reducing the sensor drift.

scholarly journals Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition

2019 ◽  
Vol 33 ◽  
pp. 8594-8601 ◽  
Author(s):  
Guanbin Li ◽  
Xin Zhu ◽  
Yirui Zeng ◽  
Qing Wang ◽  
Liang Lin
Keyword(s):  
Neural Network ◽  
Facial Expressions ◽  
Mutual Exclusion ◽  
Representation Learning ◽  
Feature Representation ◽  
Semantic Relationship ◽  
Action Unit ◽  
Facial Action ◽  
Feature Representations ◽  
Regional Feature

Facial action unit (AU) recognition is a crucial task for facial expressions analysis and has attracted extensive attention in the field of artificial intelligence and computer vision. Existing works have either focused on designing or learning complex regional feature representations, or delved into various types of AU relationship modeling. Albeit with varying degrees of progress, it is still arduous for existing methods to handle complex situations. In this paper, we investigate how to integrate the semantic relationship propagation between AUs in a deep neural network framework to enhance the feature representation of facial regions, and propose an AU semantic relationship embedded representation learning (SRERL) framework. Specifically, by analyzing the symbiosis and mutual exclusion of AUs in various facial expressions, we organize the facial AUs in the form of structured knowledge-graph and integrate a Gated Graph Neural Network (GGNN) in a multi-scale CNN framework to propagate node information through the graph for generating enhanced AU representation. As the learned feature involves both the appearance characteristics and the AU relationship reasoning, the proposed model is more robust and can cope with more challenging cases, e.g., illumination change and partial occlusion. Extensive experiments on the two public benchmarks demonstrate that our method outperforms the previous work and achieves state of the art performance.

scholarly journals Multi-Source Distilling Domain Adaptation

2020 ◽  
Vol 34 (07) ◽  
pp. 12975-12983
Author(s):  
Sicheng Zhao ◽  
Guangzhi Wang ◽  
Shanghang Zhang ◽  
Yang Gu ◽  
Yaxian Li ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Feature Space ◽  
Wasserstein Distance ◽  
Training Data ◽  
Source Distribution ◽  
Single Source ◽  
Multiple Sources ◽  
Target Domain ◽  
Target Feature ◽  
Training Samples

Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA). Conventional DA methods usually assume that the labeled data is sampled from a single source distribution. However, in practice, labeled data may be collected from multiple sources, while naive application of the single-source DA algorithms may lead to suboptimal solutions. In this paper, we propose a novel multi-source distilling domain adaptation (MDDA) network, which not only considers the different distances among multiple sources and the target, but also investigates the different similarities of the source samples to the target ones. Specifically, the proposed MDDA includes four stages: (1) pre-train the source classifiers separately using the training data from each source; (2) adversarially map the target into the feature space of each source respectively by minimizing the empirical Wasserstein distance between source and target; (3) select the source training samples that are closer to the target to fine-tune the source classifiers; and (4) classify each encoded target feature by corresponding source classifier, and aggregate different predictions using respective domain weight, which corresponds to the discrepancy between each source and target. Extensive experiments are conducted on public DA benchmarks, and the results demonstrate that the proposed MDDA significantly outperforms the state-of-the-art approaches. Our source code is released at: https://github.com/daoyuan98/MDDA.

scholarly journals Predicting Cross-Species Infection of Swine Influenza Virus with Representation Learning of Amino Acid Features

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zheng Kou ◽  
Junjie Li ◽  
Xinyue Fan ◽  
Saeed Kosari ◽  
Xiaoli Qiang
Keyword(s):  
Amino Acid ◽  
High Performance ◽  
Swine Influenza Virus ◽  
Swine Influenza ◽  
Representation Learning ◽  
Influenza Viruses ◽  
Feature Representation ◽  
Specific Information ◽  
Feature Representations ◽  
Probabilistic Information

Swine influenza viruses (SIVs) can unforeseeably cross the species barriers and directly infect humans, which pose huge challenges for public health and trigger pandemic risk at irregular intervals. Computational tools are needed to predict infection phenotype and early pandemic risk of SIVs. For this purpose, we propose a feature representation algorithm to predict cross-species infection of SIVs. We built a high-quality dataset of 1902 viruses. A feature representation learning scheme was applied to learn feature representations from 64 well-trained random forest models with multiple feature descriptors of mutant amino acid in the viral proteins, including compositional information, position-specific information, and physicochemical properties. Class and probabilistic information were integrated into the feature representations, and redundant features were removed by feature space optimization. High performance was achieved using 20 informative features and 22 probabilistic information. The proposed method will facilitate SIV characterization of transmission phenotype.

scholarly journals Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks

2015 ◽  
Vol 3 ◽  
pp. 271-282 ◽  
Author(s):  
Haitong Yang ◽  
Tao Zhuang ◽  
Chengqing Zong
Keyword(s):  
Test Data ◽  
Domain Adaptation ◽  
Feature Space ◽  
Feature Representation ◽  
Dependency Parsing ◽  
Shared Task ◽  
Target Domain ◽  
Current Systems ◽  
Semantic Dependency ◽  
Very High

In current systems for syntactic and semantic dependency parsing, people usually define a very high-dimensional feature space to achieve good performance. But these systems often suffer severe performance drops on out-of-domain test data due to the diversity of features of different domains. This paper focuses on how to relieve this domain adaptation problem with the help of unlabeled target domain data. We propose a deep learning method to adapt both syntactic and semantic parsers. With additional unlabeled target domain data, our method can learn a latent feature representation (LFR) that is beneficial to both domains. Experiments on English data in the CoNLL 2009 shared task show that our method largely reduced the performance drop on out-of-domain test data. Moreover, we get a Macro F1 score that is 2.32 points higher than the best system in the CoNLL 2009 shared task in out-of-domain tests.

scholarly journals Learning a Multi-Branch Neural Network from Multiple Sources for Knowledge Adaptation in Remote Sensing Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 1890 ◽  
Author(s):  
Mohamad Al Rahhal ◽  
Yakoub Bazi ◽  
Taghreed Abdullah ◽  
Mohamed Mekhalfi ◽  
Haikel AlHichri ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Feature Representation ◽  
Aerial Image ◽  
Multiple Sources ◽  
Target Domain ◽  
Feature Representations ◽  
Average Accuracy ◽  
The University ◽  
Multiple Domain

In this paper we propose a multi-branch neural network, called MB-Net, for solving the problem of knowledge adaptation from multiple remote sensing scene datasets acquired with different sensors over diverse locations and manually labeled with different experts. Our aim is to learn invariant feature representations from multiple source domains with labeled images and one target domain with unlabeled images. To this end, we define for MB-Net an objective function that mitigates the multiple domain shifts at both feature representation and decision levels, while retaining the ability to discriminate between different land-cover classes. The complete architecture is trainable end-to-end via the backpropagation algorithm. In the experiments, we demonstrate the effectiveness of the proposed method on a new multiple domain dataset created from four heterogonous scene datasets well known to the remote sensing community, namely, the University of California (UC-Merced) dataset, the Aerial Image dataset (AID), the PatternNet dataset, and the Northwestern Polytechnical University (NWPU) dataset. In particular, this method boosts the average accuracy over all transfer scenarios up to 89.05% compared to standard architecture based only on cross-entropy loss, which yields an average accuracy of 78.53%.

scholarly journals Domain Correction Based on Kernel Transformation for Drift Compensation in the E-Nose System

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3209 ◽  
Author(s):  
Yang Tao ◽  
Juan Xu ◽  
Zhifang Liang ◽  
Lian Xiong ◽  
Haocheng Yang
Keyword(s):  
Reproducing Kernel ◽  
Experimental Result ◽  
Target Domain ◽  
Reproducing Kernel Space ◽  
Electronic Noses ◽  
Odor Recognition ◽  
Actual Application ◽  
Sensor Drift ◽  
Drift Compensation ◽  
Effectiveness And Efficiency

This paper proposes a way for drift compensation in electronic noses (e-nose) that often suffers from uncertain and unpredictable sensor drift. Traditional machine learning methods for odor recognition require consistent data distribution, which makes the model trained with previous data less generalized. In the actual application scenario, the data collected previously and the data collected later may have different data distributions due to the sensor drift. If the dataset without sensor drift is treated as a source domain and the dataset with sensor drift as a target domain, a domain correction based on kernel transformation (DCKT) method is proposed to compensate the sensor drift. The proposed method makes the distribution consistency of two domains greatly improved through mapping to a high-dimensional reproducing kernel space and reducing the domain distance. A public benchmark sensor drift dataset is used to verify the effectiveness and efficiency of the proposed DCKT method. The experimental result shows that the proposed method yields the highest average accuracies compared to other considered methods.

scholarly journals Semi-Supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos

2021 ◽  
Vol 13 (4) ◽  
pp. 548
Author(s):  
Xiaokang Zhang ◽  
Man-On Pun ◽  
Ming Liu
Keyword(s):  
Feature Space ◽  
Representation Learning ◽  
Feature Representation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Multi Temporal ◽  
Landslide Mapping ◽  
Spatio Temporal ◽  
Reliability And Robustness ◽  
High Level

Using remote sensing techniques to monitor landslides and their resultant land cover changes is fundamentally important for risk assessment and hazard prevention. Despite enormous efforts in developing intelligent landslide mapping (LM) approaches, LM remains challenging owing to high spectral heterogeneity of very-high-resolution (VHR) images and the daunting labeling efforts. To this end, a deep learning model based on semi-supervised multi-temporal deep representation fusion network, namely SMDRF-Net, is proposed for reliable and efficient LM. In comparison with previous methods, the SMDRF-Net possesses three distinct properties. (1) Unsupervised deep representation learning at the pixel- and object-level is performed by transfer learning using the Wasserstein generative adversarial network with gradient penalty to learn discriminative deep features and retain precise outlines of landslide objects in the high-level feature space. (2) Attention-based adaptive fusion of multi-temporal and multi-level deep representations is developed to exploit the spatio-temporal dependencies of deep representations and enhance the feature representation capability of the network. (3) The network is optimized using limited samples with pseudo-labels that are automatically generated based on a comprehensive uncertainty index. Experimental results from the analysis of VHR aerial orthophotos demonstrate the reliability and robustness of the proposed approach for LM in comparison with state-of-the-art methods.

scholarly journals Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks

2018 ◽  
Author(s):  
Feiwu Yu ◽  
Xinxiao Wu ◽  
Yuchao Sun ◽  
Lixin Duan
Keyword(s):  
Domain Adaptation ◽  
Feature Representation ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Feature Representations ◽  
Adversarial Networks ◽  
Video Recognition ◽  
Novel Method ◽  
High Level ◽  
Heterogeneous Feature

Existing deep learning methods of video recognition usually require a large number of labeled videos for training. But for a new task, videos are often unlabeled and it is also time-consuming and labor-intensive to annotate them. Instead of human annotation, we try to make use of existing fully labeled images to help recognize those videos. However, due to the problem of domain shifts and heterogeneous feature representations, the performance of classifiers trained on images may be dramatically degraded for video recognition tasks. In this paper, we propose a novel method, called Hierarchical Generative Adversarial Networks (HiGAN), to enhance recognition in videos (i.e., target domain) by transferring knowledge from images (i.e., source domain). The HiGAN model consists of a \emph{low-level} conditional GAN and a \emph{high-level} conditional GAN. By taking advantage of these two-level adversarial learning, our method is capable of learning a domain-invariant feature representation of source images and target videos. Comprehensive experiments on two challenging video recognition datasets (i.e. UCF101 and HMDB51) demonstrate the effectiveness of the proposed method when compared with the existing state-of-the-art domain adaptation methods.

Iterative feature representations improve N4-methylcytosine site prediction

2019 ◽  
Vol 35 (23) ◽  
pp. 4930-4937 ◽  
Author(s):  
Leyi Wei ◽  
Ran Su ◽  
Shasha Luan ◽  
Zhijun Liao ◽  
Balachandran Manavalan ◽  
...  
Keyword(s):  
Feature Space ◽  
Feature Representation ◽  
Supplementary Information ◽  
Accurate Identification ◽  
Feature Representations ◽  
Genome Wide ◽  
A Genome ◽  
Benchmark Datasets ◽  
Sequential Models ◽  
User Friendly

Abstract Motivation Accurate identification of N4-methylcytosine (4mC) modifications in a genome wide can provide insights into their biological functions and mechanisms. Machine learning recently have become effective approaches for computational identification of 4mC sites in genome. Unfortunately, existing methods cannot achieve satisfactory performance, owing to the lack of effective DNA feature representations that are capable to capture the characteristics of 4mC modifications. Results In this work, we developed a new predictor named 4mcPred-IFL, aiming to identify 4mC sites. To represent and capture discriminative features, we proposed an iterative feature representation algorithm that enables to learn informative features from several sequential models in a supervised iterative mode. Our analysis results showed that the feature representations learnt by our algorithm can capture the discriminative distribution characteristics between 4mC sites and non-4mC sites, enlarging the decision margin between the positives and negatives in feature space. Additionally, by evaluating and comparing our predictor with the state-of-the-art predictors on benchmark datasets, we demonstrate that our predictor can identify 4mC sites more accurately. Availability and implementation The user-friendly webserver that implements the proposed 4mcPred-IFL is well established, and is freely accessible at http://server.malab.cn/4mcPred-IFL. Supplementary information Supplementary data are available at Bioinformatics online.

PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning

2019 ◽  
Vol 35 (21) ◽  
pp. 4272-4280 ◽  
Author(s):  
Leyi Wei ◽  
Chen Zhou ◽  
Ran Su ◽  
Quan Zou
Keyword(s):  
Characteristic Curve ◽  
Representation Learning ◽  
Feature Representation ◽  
Supplementary Information ◽  
Optimization Strategy ◽  
Feature Representations ◽  
Random Forest Models ◽  
Therapeutic Peptides ◽  
Functional Peptides ◽  
Robust Prediction

Abstract Motivation Prediction of therapeutic peptides is critical for the discovery of novel and efficient peptide-based therapeutics. Computational methods, especially machine learning based methods, have been developed for addressing this need. However, most of existing methods are peptide-specific; currently, there is no generic predictor for multiple peptide types. Moreover, it is still challenging to extract informative feature representations from the perspective of primary sequences. Results In this study, we have developed PEPred-Suite, a bioinformatics tool for the generic prediction of therapeutic peptides. In PEPred-Suite, we introduce an adaptive feature representation strategy that can learn the most representative features for different peptide types. To be specific, we train diverse sequence-based feature descriptors, integrate the learnt class information into our features, and utilize a two-step feature optimization strategy based on the area under receiver operating characteristic curve to extract the most discriminative features. Using the learnt representative features, we trained eight random forest models for eight different types of functional peptides, respectively. Benchmarking results showed that as compared with existing predictors, PEPred-Suite achieves better and robust performance for different peptides. As far as we know, PEPred-Suite is currently the first tool that is capable of predicting so many peptide types simultaneously. In addition, our work demonstrates that the learnt features can reliably predict different peptides. Availability and implementation The user-friendly webserver implementing the proposed PEPred-Suite is freely accessible at http://server.malab.cn/PEPred-Suite. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document