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Journal articles on the topic 'Feature Adaptation'

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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1

O'Brien, Michael J., and Thomas D. Holland. "The Role of Adaptation in Archaeological Explanation." American Antiquity 57, no. 1 (1992): 36–59. http://dx.doi.org/10.2307/2694834.

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Adaptation, a venerable icon in archaeology, often is afforded the vacuous role of being an ex-post-facto argument used to "explain" the appearance and persistence of traits among prehistoric groups—a position that has seriously impeded development of a selectionist perspective in archaeology. Biological and philosophical definitions of adaptation—and by extension, definitions of adaptedness—vary considerably, but all are far removed from those usually employed in archaeology. The prevailing view in biology is that adaptations are features that were shaped by natural selection and that increas

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Cui, Pengcheng, Yongqian Zheng, Peimin Xu, Bin Li, Mingsheng Ma, and Guiyu Zhou. "The comparison of adjoint-based grid adaptation and feature-based grid adaptation method." Journal of Physics: Conference Series 2280, no. 1 (2022): 012003. http://dx.doi.org/10.1088/1742-6596/2280/1/012003.

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Abstract Grid adaption is a popular method to enhance the resolution of flow field and the precision of numerical simulation, which automatically optimizes the grid distribution instead of manual complicated work. There exist usually two grid adaptation methods, the feature based grid adaption and adjoint based grid adaption, the former focuses on shocks, vortexes and other features of flow field, and the latter focuses on lift, drag and other aerodynamic characteristics. The comparison of adjoint based grid adaption and feature based grid adaption method is investigated in this paper. Numeric

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Chen, Cheng, Qi Dou, Hao Chen, Jing Qin, and Pheng-Ann Heng. "Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 865–72. http://dx.doi.org/10.1609/aaai.v33i01.3301865.

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This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Domain adaptation has become an important and hot topic in recent studies on deep learning, aiming to recover performance degradation when applying the neural networks to new testing domains. Our proposed SIFA is an elegant learning diagram which presents synergistic fusion of adaptations from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enha

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Qin, Ning, and Xueqiang Liu. "Flow feature aligned grid adaptation." International Journal for Numerical Methods in Engineering 67, no. 6 (2006): 787–814. http://dx.doi.org/10.1002/nme.1648.

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Li, Shuang, Chi Liu, Qiuxia Lin, et al. "Domain Conditioned Adaptation Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11386–93. http://dx.doi.org/10.1609/aaai.v34i07.6801.

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Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a

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Chen, Qingchao, and Yang Liu. "Structure-Aware Feature Fusion for Unsupervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10567–74. http://dx.doi.org/10.1609/aaai.v34i07.6629.

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Unsupervised domain Adaptation (UDA) aims to learn and transfer generalized features from a labelled source domain to a target domain without any annotations. Existing methods only aligning high-level representation but without exploiting the complex multi-class structure and local spatial structure. This is problematic as 1) the model is prone to negative transfer when the features from different classes are misaligned; 2) missing the local spatial structure poses a major obstacle in performing the fine-grained feature alignment. In this paper, we integrate the valuable information conveyed i

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Wen, Jun, Risheng Liu, Nenggan Zheng, Qian Zheng, Zhefeng Gong, and Junsong Yuan. "Exploiting Local Feature Patterns for Unsupervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5401–8. http://dx.doi.org/10.1609/aaai.v33i01.33015401.

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Unsupervised domain adaptation methods aim to alleviate performance degradation caused by domain-shift by learning domain-invariant representations. Existing deep domain adaptation methods focus on holistic feature alignment by matching source and target holistic feature distributions, without considering local features and their multi-mode statistics. We show that the learned local feature patterns are more generic and transferable and a further local feature distribution matching enables fine-grained feature alignment. In this paper, we present a method for learning domain-invariant local fe

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Sun, Feng, Hanrui Wu, Zhihang Luo, Wenwen Gu, Yuguang Yan, and Qing Du. "Informative Feature Selection for Domain Adaptation." IEEE Access 7 (2019): 142551–63. http://dx.doi.org/10.1109/access.2019.2944226.

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Li, Jingyao, Zhanshan Li, and Shuai Lü. "Feature concatenation for adversarial domain adaptation." Expert Systems with Applications 169 (May 2021): 114490. http://dx.doi.org/10.1016/j.eswa.2020.114490.

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P, Geethanjali. "Hybrid Features-Based Intrusion Detection for The Internet of Vehicles using Dynamic Adaptation." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 2308–16. http://dx.doi.org/10.22214/ijraset.2023.57839.

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Abstract: The evolving landscape of the Internet of Vehicles (IoV) has brought to the forefront a discernible array of challenges about network security. In response, this study delves into applying deep learning-based intrusion detection techniques to fortify the IoV against potential network threats. Notably, prevailing approaches often rely on a singular deep learning model for either temporal or spatial feature extraction, with a serial sequence of spatial feature extraction followed by temporal feature extraction. Such methodologies tend to exhibit shortcomings in adequately capturing the

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Stephens, John, and Sung-Ae Lee. "Transcultural Adaptation of Feature Films: South Korea’s My Sassy Girl and its Remakes." Adaptation 11, no. 1 (2018): 75–95. http://dx.doi.org/10.1093/adaptation/apy001.

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12

Zhang, Zeqing, Zuodong Gao, Xiaofan Li, Cuihua Lee, and Weiwei Lin. "Information Separation Network for Domain Adaptation Learning." Electronics 11, no. 8 (2022): 1254. http://dx.doi.org/10.3390/electronics11081254.

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The Bai People have left behind a wealth of ancient texts that record their splendid civilization, unfortunately fewer and fewer people can read these texts in the present time. Therefore, it is of great practical value to design a model that can automatically recognize the Bai ancient (offset) texts. However, due to the expert knowledge involved in the annotation of ancient (offset) texts, and its limited scale, we consider that using handwritten Bai texts to help identify ancient (offset) Bai texts for handwritten Bai texts can be easily obtained and annotated. Essentially, this is a problem

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13

Gardner, Andy. "The purpose of adaptation." Interface Focus 7, no. 5 (2017): 20170005. http://dx.doi.org/10.1098/rsfs.2017.0005.

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A central feature of Darwin's theory of natural selection is that it explains the purpose of biological adaptation. Here, I: emphasize the scientific importance of understanding what adaptations are for, in terms of facilitating the derivation of empirically testable predictions; discuss the population genetical basis for Darwin's theory of the purpose of adaptation, with reference to Fisher's ‘fundamental theorem of natural selection'; and show that a deeper understanding of the purpose of adaptation is achieved in the context of social evolution, with reference to inclusive fitness and super

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Wang, Wei, Hao Wang, Zhi-Yong Ran, and Ran He. "Learning Robust Feature Transformation for Domain Adaptation." Pattern Recognition 114 (June 2021): 107870. http://dx.doi.org/10.1016/j.patcog.2021.107870.

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Jung, Ho-Young Jung, Mansoo Park Park, Hoi-Rin Kim Kim, and Minsoo Hahn Hahn. "Speaker Adaptation Using ICA-Based Feature Transformation." ETRI Journal 24, no. 6 (2002): 469–72. http://dx.doi.org/10.4218/etrij.02.0202.0003.

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Akçay, Çağlar, and Eliot Hazeltine. "Domain-specific conflict adaptation without feature repetitions." Psychonomic Bulletin & Review 18, no. 3 (2011): 505–11. http://dx.doi.org/10.3758/s13423-011-0084-y.

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Wen, Jun, Junsong Yuan, Qian Zheng, Risheng Liu, Zhefeng Gong, and Nenggan Zheng. "Hierarchical domain adaptation with local feature patterns." Pattern Recognition 124 (April 2022): 108445. http://dx.doi.org/10.1016/j.patcog.2021.108445.

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JIANG, Chengming, Changyong JIAO, Huahua DONG, Wuheng ZUO, Lian XU, and Fengpei HU. "Cross-category Face Adaptation of Feature Association." Acta Psychologica Sinica 46, no. 8 (2014): 1072. http://dx.doi.org/10.3724/sp.j.1041.2014.01072.

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Lee, Hyeopwoo, and Dongsuk Yook. "Feature adaptation for robust mobile speech recognition." IEEE Transactions on Consumer Electronics 58, no. 4 (2012): 1393–98. http://dx.doi.org/10.1109/tce.2012.6415011.

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Prabuchandran, K. J., Shalabh Bhatnagar, and Vivek S. Borkar. "Actor-Critic Algorithms with Online Feature Adaptation." ACM Transactions on Modeling and Computer Simulation 26, no. 4 (2016): 1–26. http://dx.doi.org/10.1145/2868723.

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21

Tahmoresnezhad, Jafar, and Sattar Hashemi. "Visual domain adaptation via transfer feature learning." Knowledge and Information Systems 50, no. 2 (2016): 585–605. http://dx.doi.org/10.1007/s10115-016-0944-x.

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22

Wu, Lan, Chongyang Li, Qiliang Chen, and Binquan Li. "Deep adversarial domain adaptation network." International Journal of Advanced Robotic Systems 17, no. 5 (2020): 172988142096464. http://dx.doi.org/10.1177/1729881420964648.

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The advantage of adversarial domain adaptation is that it uses the idea of adversarial adaptation to confuse the feature distribution of two domains and solve the problem of domain transfer in transfer learning. However, although the discriminator completely confuses the two domains, adversarial domain adaptation still cannot guarantee the consistent feature distribution of the two domains, which may further deteriorate the recognition accuracy. Therefore, in this article, we propose a deep adversarial domain adaptation network, which optimises the feature distribution of the two confused doma

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23

Lin, Hui-shan. "Loanword adaptation of Japanese consonants in Kavalan." Concentric. Studies in Linguistics 49, no. 2 (2023): 139–74. http://dx.doi.org/10.1075/consl.22045.lin.

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Abstract This paper is a first attempt to investigate loanword adaptation of Japanese consonants in Kavalan. Based on first hand data, the paper shows that when a Japanese consonant is adapted as the closest Kavalan consonant, the manner features are of higher phonological weight and are more faithfully retained than the place and voicing features. It is shown that for adaptations involving a change in the place of articulation, the change is generally minimal and confined within the same major place feature. This is except for the [ɸ] > [h] and the [ç] > [h] mappings which involve a cha

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Xiao, Ting, Cangning Fan, Peng Liu, and Hongwei Liu. "Simultaneously Improve Transferability and Discriminability for Adversarial Domain Adaptation." Entropy 24, no. 1 (2021): 44. http://dx.doi.org/10.3390/e24010044.

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Although adversarial domain adaptation enhances feature transferability, the feature discriminability will be degraded in the process of adversarial learning. Moreover, most domain adaptation methods only focus on distribution matching in the feature space; however, shifts in the joint distributions of input features and output labels linger in the network, and thus, the transferability is not fully exploited. In this paper, we propose a matrix rank embedding (MRE) method to enhance feature discriminability and transferability simultaneously. MRE restores a low-rank structure for data in the s

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Fu, Hongliang, Zhihao Zhuang, Yang Wang, Chen Huang, and Wenzhuo Duan. "Cross-Corpus Speech Emotion Recognition Based on Multi-Task Learning and Subdomain Adaptation." Entropy 25, no. 1 (2023): 124. http://dx.doi.org/10.3390/e25010124.

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To solve the problem of feature distribution discrepancy in cross-corpus speech emotion recognition tasks, this paper proposed an emotion recognition model based on multi-task learning and subdomain adaptation, which alleviates the impact on emotion recognition. Existing methods have shortcomings in speech feature representation and cross-corpus feature distribution alignment. The proposed model uses a deep denoising auto-encoder as a shared feature extraction network for multi-task learning, and the fully connected layer and softmax layer are added before each recognition task as task-specifi

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Chen, Huan, Farong Gao, and Qizhong Zhang. "FDDS: Feature Disentangling and Domain Shifting for Domain Adaptation." Mathematics 11, no. 13 (2023): 2995. http://dx.doi.org/10.3390/math11132995.

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Domain adaptation is a learning strategy that aims to improve the performance of models in the current field by leveraging similar domain information. In order to analyze the effects of feature disentangling on domain adaptation and evaluate a model’s suitability in the original scene, we present a method called feature disentangling and domain shifting (FDDS) for domain adaptation. FDDS utilizes sample information from both the source and target domains, employing a non-linear disentangling approach and incorporating learnable weights to dynamically separate content and style features. Additi

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Chen, Minghao, Shuai Zhao, Haifeng Liu, and Deng Cai. "Adversarial-Learned Loss for Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3521–28. http://dx.doi.org/10.1609/aaai.v34i04.5757.

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Recently, remarkable progress has been made in learning transferable representation across domains. Previous works in domain adaptation are majorly based on two techniques: domain-adversarial learning and self-training. However, domain-adversarial learning only aligns feature distributions between domains but does not consider whether the target features are discriminative. On the other hand, self-training utilizes the model predictions to enhance the discrimination of target features, but it is unable to explicitly align domain distributions. In order to combine the strengths of these two met

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Chen, Chao, Zhihong Chen, Boyuan Jiang, and Xinyu Jin. "Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3296–303. http://dx.doi.org/10.1609/aaai.v33i01.33013296.

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Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domain alignment approaches can only reduce, but not remove the domain shift, target domain samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the hyperplane learned from the source domain. To alleviate th

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Wang, Meng-zhu. "SimProF: A Simple Probabilistic Framework for Unsupervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21153–61. https://doi.org/10.1609/aaai.v39i20.35413.

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Unsupervised domain adaptation (UDA) aims at knowledge transfer from a labeled source domain to an unlabeled target domain. Most UDA techniques achieve this by reducing feature discrepancies between the two domains to learn domain-invariant feature representations. In this paper, we enhance this approach by proposing a simple yet powerful probabilistic framework (SimProF) for UDA to minimize the domain gap between the two domains. SimProF estimates the feature space distribution for each class and generates contrastive pairs by leveraging the shared categories between the source and target dom

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Lee, Seongmin, Hyunsik Jeon, and U. Kang. "Multi-EPL: Accurate multi-source domain adaptation." PLOS ONE 16, no. 8 (2021): e0255754. http://dx.doi.org/10.1371/journal.pone.0255754.

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Given multiple source datasets with labels, how can we train a target model with no labeled data? Multi-source domain adaptation (MSDA) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels. MSDA is a crucial problem applicable to many practical cases where labels for the target data are unavailable due to privacy issues. Existing MSDA frameworks are limited since they align data without considering labels of the features of each domain. They also do not fully utilize the target data without labels and rely on limited feature

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Liu, Chengyao, and Fei Dong. "A New Framework Based on Supervised Joint Distribution Adaptation for Bearing Fault Diagnosis across Diverse Working Conditions." Shock and Vibration 2024 (January 3, 2024): 1–24. http://dx.doi.org/10.1155/2024/8296809.

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To address the degradation of diagnostic performance due to data distribution differences and the scarcity of labeled fault data, this study has focused on transfer learning-based cross-domain fault diagnosis, which attracts considerable attention. However, deep transfer learning-based methods often present a challenge due to their time-consuming and costly nature, particularly in tuning hyperparameters. For this issue, on the basis of classical features-based transfer learning method, this study introduces a new framework for bearing fault diagnosis based on supervised joint distribution adap

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Zhang, Yutong, Xin Lyu, Xin Li, et al. "Complementary Local–Global Optimization for Few-Shot Object Detection in Remote Sensing." Remote Sensing 17, no. 13 (2025): 2136. https://doi.org/10.3390/rs17132136.

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Few-shot object detection (FSOD) in remote sensing remains challenging due to the scarcity of annotated samples and the complex background environments in aerial images. Existing methods often struggle to capture fine-grained local features or suffer from bias during global adaptation to novel categories, leading to misclassification as background. To address these issues, we propose a framework that simultaneously enhances local feature learning and global feature adaptation. Specifically, we design an Extensible Local Feature Aggregator Module (ELFAM) that reconstructs object structures via

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Li, Xudong, Jianhua Zheng, Mingtao Li, Wenzhen Ma, and Yang Hu. "Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis." Sensors 21, no. 2 (2021): 450. http://dx.doi.org/10.3390/s21020450.

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In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method can solve the problem of the unlabeled testing dataset in transfer learning. Moreover, Convolutional Neural Network (CNN) is the most widely used network among existing domain adaptation approaches due to its powerful feature extraction capability. However, network designing is too empirical, and there is no network designing principle from the fr

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Li, Xudong, Jianhua Zheng, Mingtao Li, Wenzhen Ma, and Yang Hu. "Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis." Sensors 21, no. 2 (2021): 450. http://dx.doi.org/10.3390/s21020450.

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In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method can solve the problem of the unlabeled testing dataset in transfer learning. Moreover, Convolutional Neural Network (CNN) is the most widely used network among existing domain adaptation approaches due to its powerful feature extraction capability. However, network designing is too empirical, and there is no network designing principle from the fr

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Wang, Meng, and Jiawei Fu. "A Triple Adversary Network Driven by Hybrid High-Order Attention for Domain Adaptation." Electronics 9, no. 12 (2020): 2121. http://dx.doi.org/10.3390/electronics9122121.

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How to bridge the knowledge gap between the annotated source domain and the unlabeled target domain is a basic challenge to domain adaptation. The existing approaches can relieve this gap by feature alignments across domains; however, aligning non-transferable features may lead to negative shift confusing the knowledge learning on target domains. In this paper, a triple adversary network is proposed on the basis of a high-order attention, hopefully to solve the problem. The proposed architecture focuses on the detailed feature alignment by a hybrid high-order attention using a fast iteration a

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Wang, Yucheng, Yuecong Xu, Jianfei Yang, et al. "SEnsor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10253–61. http://dx.doi.org/10.1609/aaai.v37i8.26221.

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Unsupervised Domain Adaptation (UDA) methods can reduce label dependency by mitigating the feature discrepancy between labeled samples in a source domain and unlabeled samples in a similar yet shifted target domain. Though achieving good performance, these methods are inapplicable for Multivariate Time-Series (MTS) data. MTS data are collected from multiple sensors, each of which follows various distributions. However, most UDA methods solely focus on aligning global features but cannot consider the distinct distributions of each sensor. To cope with such concerns, a practical domain adaptatio

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Yu, Lei, Wanqi Yang, Shengqi Huang, Lei Wang, and Ming Yang. "High-Level Semantic Feature Matters Few-Shot Unsupervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 11025–33. http://dx.doi.org/10.1609/aaai.v37i9.26306.

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In few-shot unsupervised domain adaptation (FS-UDA), most existing methods followed the few-shot learning (FSL) methods to leverage the low-level local features (learned from conventional convolutional models, e.g., ResNet) for classification. However, the goal of FS-UDA and FSL are relevant yet distinct, since FS-UDA aims to classify the samples in target domain rather than source domain. We found that the local features are insufficient to FS-UDA, which could introduce noise or bias against classification, and not be used to effectively align the domains. To address the above issues, we aim

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Gao, Yuan, Peipeng Chen, Yue Gao, Jinpeng Wang, YoungSun Pan, and Andy J. Ma. "Hierarchical feature disentangling network for universal domain adaptation." Pattern Recognition 127 (July 2022): 108616. http://dx.doi.org/10.1016/j.patcog.2022.108616.

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Rakshit, Sayan, Anwesh Mohanty, Ruchika Chavhan, Biplab Banerjee, Gemma Roig, and Subhasis Chaudhuri. "FRIDA — Generative feature replay for incremental domain adaptation." Computer Vision and Image Understanding 217 (March 2022): 103367. http://dx.doi.org/10.1016/j.cviu.2022.103367.

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Zhao, Peng, Wenhua Zang, Bin Liu, et al. "Domain adaptation with feature and label adversarial networks." Neurocomputing 439 (June 2021): 294–301. http://dx.doi.org/10.1016/j.neucom.2021.01.062.

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Zhang, Tianliang, Zhenjun Han, Huijuan Xu, Baochang Zhang, and Qixiang Ye. "CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection." IEEE Transactions on Intelligent Transportation Systems 21, no. 11 (2020): 4593–604. http://dx.doi.org/10.1109/tits.2019.2942045.

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Kumagai, Atsutoshi, Tomoharu Iwata, and Yasuhiro Fujiwara. "Zero-Shot Task Adaptation with Relevant Feature Information." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13283–91. http://dx.doi.org/10.1609/aaai.v38i12.29229.

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We propose a method to learn prediction models such as classifiers for unseen target tasks where labeled and unlabeled data are absent but a few relevant input features for solving the tasks are given. Although machine learning requires data for training, data are often difficult to collect in practice. On the other hand, for many applications, a few relevant features would be more easily obtained. Although zero-shot learning or zero-shot domain adaptation use external knowledge to adapt to unseen classes or tasks without data, relevant features have not been used in existing studies. The prop

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Wang, Kai, Wei Zhao, Aidong Xu, Peng Zeng, and Shunkun Yang. "One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions." Sensors 20, no. 21 (2020): 6039. http://dx.doi.org/10.3390/s20216039.

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Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-quality labeled data. However, machines usually work under different working conditions in practice, which challenges these prerequisites due to the fact that the data distributions under different working conditions are different. In this paper, the one-dimensional Multi-Scale Domain Adaptive Network (1D-MSDAN) is proposed to address this issue. The 1

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Yan, Jingjie, Yuebo Yue, Kai Yu, et al. "Multi-Representation Joint Dynamic Domain Adaptation Network for Cross-Database Facial Expression Recognition." Electronics 13, no. 8 (2024): 1470. http://dx.doi.org/10.3390/electronics13081470.

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In order to obtain more fine-grained information from multiple sub-feature spaces for domain adaptation, this paper proposes a novel multi-representation joint dynamic domain adaptation network (MJDDAN) and applies it to achieve cross-database facial expression recognition. The MJDDAN uses a hybrid structure to extract multi-representation features and maps the original facial expression features into multiple sub-feature spaces, aligning the expression features of the source domain and target domain in multiple sub-feature spaces from different angles to extract features more comprehensively.

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Zheng, Xiaorong, Zhaojian Gu, Caiming Liu, Jiahao Jiang, Zhiwei He, and Mingyu Gao. "Deep Transfer Network with Multi-Space Dynamic Distribution Adaptation for Bearing Fault Diagnosis." Entropy 24, no. 8 (2022): 1122. http://dx.doi.org/10.3390/e24081122.

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Domain adaptation-based bearing fault diagnosis methods have recently received high attention. However, the extracted features in these methods fail to adequately represent fault information due to the versatility of the work scenario. Moreover, most existing adaptive methods attempt to align the feature space of domains by calculating the sum of marginal distribution distance and conditional distribution distance, without considering variable cross-domain diagnostic scenarios that provide significant cues for fault diagnosis. To address the above problems, we propose a deep convolutional mult

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Yue, Haixiao, Keyao Wang, Guosheng Zhang, et al. "Cyclically Disentangled Feature Translation for Face Anti-spoofing." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3358–66. http://dx.doi.org/10.1609/aaai.v37i3.25443.

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Current domain adaptation methods for face anti-spoofing leverage labeled source domain data and unlabeled target domain data to obtain a promising generalizable decision boundary. However, it is usually difficult for these methods to achieve a perfect domain-invariant liveness feature disentanglement, which may degrade the final classification performance by domain differences in illumination, face category, spoof type, etc. In this work, we tackle cross-scenario face anti-spoofing by proposing a novel domain adaptation method called cyclically disentangled feature translation network (CDFTN)

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Ding, Xinmin, Zenghui Zhang, Kun Wang, Xiaolin Xiao, and Minpeng Xu. "A Lightweight Network with Domain Adaptation for Motor Imagery Recognition." Entropy 27, no. 1 (2024): 14. https://doi.org/10.3390/e27010014.

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Brain–computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation. A lightweight feature extraction module is designed to extract key features from both the source and target doma

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Kumagai, Atsutoshi, and Tomoharu Iwata. "Unsupervised Domain Adaptation by Matching Distributions Based on the Maximum Mean Discrepancy via Unilateral Transformations." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4106–13. http://dx.doi.org/10.1609/aaai.v33i01.33014106.

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We propose a simple yet effective method for unsupervised domain adaptation. When training and test distributions are different, standard supervised learning methods perform poorly. Semi-supervised domain adaptation methods have been developed for the case where labeled data in the target domain are available. However, the target data are often unlabeled in practice. Therefore, unsupervised domain adaptation, which does not require labels for target data, is receiving a lot of attention. The proposed method minimizes the discrepancy between the source and target distributions of input features

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Yang, Jihan, Ruijia Xu, Ruiyu Li, et al. "An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12613–20. http://dx.doi.org/10.1609/aaai.v34i07.6952.

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We focus on Unsupervised Domain Adaptation (UDA) for the task of semantic segmentation. Recently, adversarial alignment has been widely adopted to match the marginal distribution of feature representations across two domains globally. However, this strategy fails in adapting the representations of the tail classes or small objects for semantic segmentation since the alignment objective is dominated by head categories or large objects. In contrast to adversarial alignment, we propose to explicitly train a domain-invariant classifier by generating and defensing against pointwise feature space ad

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Lee, Seungmin, and Kyupil Yeon. "A Copula Based Unsupervised Domain Adaptation for Image Classification." Korean Data Analysis Society 26, no. 2 (2024): 433–44. http://dx.doi.org/10.37727/jkdas.2024.26.2.433.

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In this paper, we present an unsupervised domain adaptation algorithm for image classification using principal component analysis (PCA) and Gaussian copula function alignment. The motivation of the proposed algorithm stems from the idea of CORAL algorithm which extracts domain invariant features by aligning the correlation structure between a source and a target domain. However, it suffers from the fact that highly skewed marginal distributions happen to distort the correlation structure so that it may cause a negative transfer. Therefore we utilize a copula function that enables us to analyze

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