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Journal articles on the topic 'Target domain'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Cao, Meng, and Songcan Chen. "Mixup-Induced Domain Extrapolation for Domain Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11168–76. http://dx.doi.org/10.1609/aaai.v38i10.28994.

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Domain generalization aims to learn a well-performed classifier on multiple source domains for unseen target domains under domain shift. Domain-invariant representation (DIR) is an intuitive approach and has been of great concern. In practice, since the targets are variant and agnostic, only a few sources are not sufficient to reflect the entire domain population, leading to biased DIR. Derived from PAC-Bayes framework, we provide a novel generalization bound involving the number of domains sampled from the environment (N) and the radius of the Wasserstein ball centred on the target (r), which

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Lu, Yuwu, Xue Hu, Waikeung Wong, and Haoyu Huang. "Collaborative Semantic Consistency Alignment for Blended-Target Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19170–79. https://doi.org/10.1609/aaai.v39i18.34110.

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Blended-target domain adaptation (BTDA) leverages learned source knowledge to adapt the model to a blended-target domain that is composed of multiple unlabeled sub-target domains with distinct statistical characteristics. The existing BTDA methods usually overlook semantic correlation information across multiple domains and domain shifts among sub-target domains, resulting in suboptimal adaptation performance. To fully harness semantic knowledge and alleviate domain shifts in hybrid data distribution, we propose a collaborative semantic consistency alignment (CSCA) method for BTDA. Specificall

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Xu, Pengcheng, Boyu Wang, and Charles Ling. "Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3036–44. http://dx.doi.org/10.1609/aaai.v37i3.25407.

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Current methods of blended targets domain adaptation (BTDA) usually infer or consider domain label information but underemphasize hybrid categorical feature structures of targets, which yields limited performance, especially under the label distribution shift. We demonstrate that domain labels are not directly necessary for BTDA if categorical distributions of various domains are sufficiently aligned even facing the imbalance of domains and the label distribution shift of classes. However, we observe that the cluster assumption in BTDA does not comprehensively hold. The hybrid categorical feat

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Wang, Yu, Ronghang Zhu, Pengsheng Ji, and Sheng Li. "Open-Set Graph Domain Adaptation via Separate Domain Alignment." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 9142–50. http://dx.doi.org/10.1609/aaai.v38i8.28765.

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Domain adaptation has become an attractive learning paradigm, as it can leverage source domains with rich labels to deal with classification tasks in an unlabeled target domain. A few recent studies develop domain adaptation approaches for graph-structured data. In the case of node classification task, current domain adaptation methods only focus on the closed-set setting, where source and target domains share the same label space. A more practical assumption is that the target domain may contain new classes that are not included in the source domain. Therefore, in this paper, we introduce a n

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Liu, Ming, Dong Ren, Hang Sun, and Simon X. Yang. "Multibranch Unsupervised Domain Adaptation Network for Cross Multidomain Orchard Area Segmentation." Remote Sensing 14, no. 19 (2022): 4915. http://dx.doi.org/10.3390/rs14194915.

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Although unsupervised domain adaptation (UDA) has been extensively studied in remote sensing image segmentation tasks, most UDA models are designed based on single-target domain settings. Large-scale remote sensing images often have multiple target domains in practical applications, and the simple extension of single-target UDA models to multiple target domains is unstable and costly. Multi-target unsupervised domain adaptation (MTUDA) is a more practical scenario that has great potential for solving the problem of crossing multiple domains in remote sensing images. However, existing MTUDA mod

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Lu, Yuwu, Haoyu Huang, Waikeung Wong, and Xue Hu. "Invertible Projection and Conditional Alignment for Multi-Source Blended-Target Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19180–88. https://doi.org/10.1609/aaai.v39i18.34111.

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Multi-source domain adaptation (MSDA), which utilizes multiple source domains to align the distribution of a single target domain, is a popular and challenging setting in domain adaptation (DA). However, existing MSDA approaches are difficult to obtain sufficient target domain knowledge, which serve as the transfer object. Furthermore, the target distributions are confused in the real world, i.e., the model cannot obtain the domain labels of target domains. To tackle these problems, we consider a more realistic DA setting Multi-Source Blended-Target Domain Adaptation (MBDA) and propose an Inve

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Ye, Fei, and Mingjie Zhang. "Structures and target recognition modes of PDZ domains: recurring themes and emerging pictures." Biochemical Journal 455, no. 1 (2013): 1–14. http://dx.doi.org/10.1042/bj20130783.

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PDZ domains are highly abundant protein–protein interaction modules and are often found in multidomain scaffold proteins. PDZ-domain-containing scaffold proteins regulate multiple biological processes, including trafficking and clustering receptors and ion channels at defined membrane regions, organizing and targeting signalling complexes at specific cellular compartments, interfacing cytoskeletal structures with membranes, and maintaining various cellular structures. PDZ domains, each with ~90-amino-acid residues folding into a highly similar structure, are best known to bind to short C-termi

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Choi, Jongwon, Youngjoon Choi, Jihoon Kim, et al. "Visual Domain Adaptation by Consensus-Based Transfer to Intermediate Domain." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10655–62. http://dx.doi.org/10.1609/aaai.v34i07.6692.

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We describe an unsupervised domain adaptation framework for images by a transform to an abstract intermediate domain and ensemble classifiers seeking a consensus. The intermediate domain can be thought as a latent domain where both the source and target domains can be transferred easily. The proposed framework aligns both domains to the intermediate domain, which greatly improves the adaptation performance when the source and target domains are notably dissimilar. In addition, we propose an ensemble model trained by confusing multiple classifiers and letting them make a consensus alternately t

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Xu, Yifan, Kekai Sheng, Weiming Dong, Baoyuan Wu, Changsheng Xu, and Bao-Gang Hu. "Towards Corruption-Agnostic Robust Domain Adaptation." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 4 (2022): 1–16. http://dx.doi.org/10.1145/3501800.

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Great progress has been achieved in domain adaptation in decades. Existing works are always based on an ideal assumption that testing target domains are independent and identically distributed with training target domains. However, due to unpredictable corruptions (e.g., noise and blur) in real data, such as web images and real-world object detection, domain adaptation methods are increasingly required to be corruption robust on target domains. We investigate a new task, corruption-agnostic robust domain adaptation (CRDA), to be accurate on original data and robust against unavailable-for-trai

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Wei, Yikang, and Yahong Han. "Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15805–13. http://dx.doi.org/10.1609/aaai.v38i14.29510.

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Federated Domain Generalization aims to learn a domain-invariant model from multiple decentralized source domains for deployment on unseen target domain. Due to privacy concerns, the data from different source domains are kept isolated, which poses challenges in bridging the domain gap. To address this issue, we propose a Multi-source Collaborative Gradient Discrepancy Minimization (MCGDM) method for federated domain generalization. Specifically, we propose intra-domain gradient matching between the original images and augmented images to avoid overfitting the domain-specific information withi

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GLYNN, Paul. "Neuropathy target esterase." Biochemical Journal 344, no. 3 (1999): 625–31. http://dx.doi.org/10.1042/bj3440625.

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Neuropathy target esterase (NTE) is an integral membrane protein present in all neurons and in some non-neural-cell types of vertebrates. Recent data indicate that NTE is involved in a cell-signalling pathway controlling interactions between neurons and accessory glial cells in the developing nervous system. NTE has serine esterase activity and efficiently catalyses the hydrolysis of phenyl valerate (PV) in vitro, but its physiological substrate is unknown. By sequence analysis NTE has been found to be related neither to the major serine esterase family, which includes acetylcholinesterase, no

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Li, Haoliang, Wen Li, and Shiqi Wang. "Discovering and incorporating latent target-domains for domain adaptation." Pattern Recognition 108 (December 2020): 107536. http://dx.doi.org/10.1016/j.patcog.2020.107536.

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Wang, Yong, Zhehao Shu, Yinzhi Feng, et al. "Enhancing Cross-Domain Remote Sensing Scene Classification by Multi-Source Subdomain Distribution Alignment Network." Remote Sensing 17, no. 7 (2025): 1302. https://doi.org/10.3390/rs17071302.

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Multi-source domain adaptation (MSDA) in remote sensing (RS) scene classification has recently gained significant attention in the visual recognition community. It leverages multiple well-labeled source domains to train a model capable of achieving strong generalization on the target domain with little to no labeled data from the target domain. However, the distribution shifts among multiple source domains make it more challenging to align the distributions between the target domain and all source domains concurrently. Moreover, relying solely on global alignment risks losing fine-grained info

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Wu, Jiahua, and Yuchun Fang. "Towards Discriminative Class-Aware Domain Alignment via Coding Rate Reduction for Unsupervised Adversarial Domain Adaptation." Symmetry 16, no. 9 (2024): 1216. http://dx.doi.org/10.3390/sym16091216.

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Unsupervised domain adaptation (UDA) methods, based on adversarial learning, employ the means of implicit global and class-aware domain alignment to learn the symmetry between source and target domains and facilitate the transfer of knowledge from a labeled source domain to an unlabeled target domain. However, these methods still face misalignment and poor target generalization due to small inter-class domain discrepancy and large intra-class discrepancy of target features. To tackle these challenges, we introduce a novel adversarial learning-based UDA framework named Coding Rate Reduction Adv

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Jin, Wei, and Nan Jia. "Learning Transferable Convolutional Proxy by SMI-Based Matching Technique." Shock and Vibration 2020 (October 14, 2020): 1–15. http://dx.doi.org/10.1155/2020/8873137.

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Domain-transfer learning is a machine learning task to explore a source domain data set to help the learning problem in a target domain. Usually, the source domain has sufficient labeled data, while the target domain does not. In this paper, we propose a novel domain-transfer convolutional model by mapping a target domain data sample to a proxy in the source domain and applying a source domain model to the proxy for the purpose of prediction. In our framework, we firstly represent both source and target domains to feature vectors by two convolutional neural networks and then construct a proxy

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Song, Rui, Fausto Giunchiglia, Yingji Li, Mingjie Tian, and Hao Xu. "TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 18999–9007. http://dx.doi.org/10.1609/aaai.v38i17.29866.

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Cross-domain text classification aims to transfer models from label-rich source domains to label-poor target domains, giving it a wide range of practical applications. Many approaches promote cross-domain generalization by capturing domaininvariant features. However, these methods rely on unlabeled samples provided by the target domains, which renders the model ineffective when the target domain is agnostic. Furthermore, the models are easily disturbed by shortcut learning in the source domain, which also hinders the improvement of domain generalization ability. To solve the aforementioned iss

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Hu, Xuming, Zhaochen Hong, Yong Jiang, et al. "Three Heads Are Better than One: Improving Cross-Domain NER with Progressive Decomposed Network." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 18261–69. http://dx.doi.org/10.1609/aaai.v38i16.29785.

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Cross-domain named entity recognition (NER) tasks encourage NER models to transfer knowledge from data-rich source domains to sparsely labeled target domains. Previous works adopt the paradigms of pre-training on the source domain followed by fine-tuning on the target domain. However, these works ignore that general labeled NER source domain data can be easily retrieved in the real world, and soliciting more source domains could bring more benefits. Unfortunately, previous paradigms cannot efficiently transfer knowledge from multiple source domains. In this work, to transfer multiple source do

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Yang, Qihong, Ruijun Jing, and Jiliang Mu. "Multi-Modal MR Image Segmentation Strategy for Brain Tumors Based on Domain Adaptation." Computers 13, no. 12 (2024): 347. https://doi.org/10.3390/computers13120347.

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During the study of multimodal brain tumor MR image segmentation, the large differences in the image distributions make the assumption that the conditional probabilities are similar when the edge distributions of the target and source domains are similar, and that the edge distributions are similar when the conditional probabilities are similar, not valid. In addition, the training network is usually trained on single domain data, which creates a tendency for the network to represent the image towards the source domain when the target domain is not labeled. Based on the aforementioned reasons,

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Zhao, S., S. Saha, and X. X. Zhu. "GRAPH NEURAL NETWORK BASED OPEN-SET DOMAIN ADAPTATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 31, 2022): 1407–13. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1407-2022.

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Abstract. Owing to the presence of many sensors and geographic/seasonal variations, domain adaptation is an important topic in remote sensing. However, most domain adaptation methods focus on close-set adaptation, i.e., they assume that the source and target domains share the same label space. This assumption often does not hold in practice, as there can be previously unseen classes in the target domain. To circumnavigate this issue, we propose a method for open set domain adaptation, where the target domain contains additional unknown classes that are not present in the source domain. To impr

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Doğan, Tunca, Ece Akhan Güzelcan, Marcus Baumann, et al. "Protein domain-based prediction of drug/compound–target interactions and experimental validation on LIM kinases." PLOS Computational Biology 17, no. 11 (2021): e1009171. http://dx.doi.org/10.1371/journal.pcbi.1009171.

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Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins’ structure/function, and bias in system training datasets. Here, we propose a new method “DRUIDom” (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome probl

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Xu, Minghao, Jian Zhang, Bingbing Ni, et al. "Adversarial Domain Adaptation with Domain Mixup." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6502–9. http://dx.doi.org/10.1609/aaai.v34i04.6123.

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Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains. However, two common limitations exist in current adversarial-learning-based methods. First, samples from two domains alone are not sufficient to ensure domain-invariance at most part of latent space. Second, the domain discriminator involved in these methods can only judge real or fake with the guidance of hard label, while it is more reasonable to use soft scores to evaluate the generated images or features, i.e., to fully utilize the inter-domain in

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Xie, Haonan, Hao Luo, Jianyang Gu, and Wei Jiang. "Unsupervised Domain Adaptive Person Re-Identification via Intermediate Domains." Applied Sciences 12, no. 14 (2022): 6990. http://dx.doi.org/10.3390/app12146990.

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Recent years have witnessed outstanding success in supervised domain adaptive person re-identification (ReID). However, the model often suffers serious performance drops when transferring to another domain in real-world applications. To address the domain gap situations, many unsupervised domain adaptive (UDA) methods have been proposed to adapt the model trained on the source domain to a target domain. Such methods are typically based on clustering algorithms to generate pseudo labels. Noisy labels, which often exist due to the instability of clustering algorithms, will substantially affect t

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Liu, Daizong, Xiang Fang, Xiaoye Qu, et al. "Unsupervised Domain Adaptative Temporal Sentence Localization with Mutual Information Maximization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3567–75. http://dx.doi.org/10.1609/aaai.v38i4.28145.

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Temporal sentence localization (TSL) aims to localize a target segment in a video according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on abundant yet expensive manual annotations for training. Moreover, these trained data-dependent models usually can not generalize well to unseen scenarios because of the inherent domain shift. To facilitate this issue, in this paper, we target another more practical but challenging setting: unsupervised domain adaptative temporal sentence localization (UDA-TSL), which explores whether the

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Cuong, Hoang, Khalil Sima’an, and Ivan Titov. "Adapting to All Domains at Once: Rewarding Domain Invariance in SMT." Transactions of the Association for Computational Linguistics 4 (December 2016): 99–112. http://dx.doi.org/10.1162/tacl_a_00086.

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Existing work on domain adaptation for statistical machine translation has consistently assumed access to a small sample from the test distribution (target domain) at training time. In practice, however, the target domain may not be known at training time or it may change to match user needs. In such situations, it is natural to push the system to make safer choices, giving higher preference to domain-invariant translations, which work well across domains, over risky domain-specific alternatives. We encode this intuition by (1) inducing latent subdomains from the training data only; (2) introd

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Yang, Guanglei, Haifeng Xia, Mingli Ding, and Zhengming Ding. "Bi-Directional Generation for Unsupervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6615–22. http://dx.doi.org/10.1609/aaai.v34i04.6137.

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Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. The conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. To balance the mitigation of domain gap and the preservation of the inherent structure, we propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains. Specifically, two cross-domain generators are employed to synthesize on

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Wang, Jie, Kaibin Tian, Dayong Ding, Gang Yang, and Xirong Li. "Unsupervised Domain Expansion for Visual Categorization." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 4 (2021): 1–24. http://dx.doi.org/10.1145/3448108.

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Expanding visual categorization into a novel domain without the need of extra annotation has been a long-term interest for multimedia intelligence. Previously, this challenge has been approached by unsupervised domain adaptation (UDA). Given labeled data from a source domain and unlabeled data from a target domain, UDA seeks for a deep representation that is both discriminative and domain-invariant. While UDA focuses on the target domain, we argue that the performance on both source and target domains matters, as in practice which domain a test example comes from is unknown. In this article, w

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Li, Jingmei, Weifei Wu, Di Xue, and Peng Gao. "Multi-Source Deep Transfer Neural Network Algorithm." Sensors 19, no. 18 (2019): 3992. http://dx.doi.org/10.3390/s19183992.

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Transfer learning can enhance classification performance of a target domain with insufficient training data by utilizing knowledge relating to the target domain from source domain. Nowadays, it is common to see two or more source domains available for knowledge transfer, which can improve performance of learning tasks in the target domain. However, the classification performance of the target domain decreases due to mismatching of probability distribution. Recent studies have shown that deep learning can build deep structures by extracting more effective features to resist the mismatching. In

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Wang, Yiqun, Hui Huang, and Radu State. "Cross Domain Early Crop Mapping with Label Spaces Discrepancies using MultiCropGAN." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1-2024 (May 9, 2024): 241–48. http://dx.doi.org/10.5194/isprs-annals-x-1-2024-241-2024.

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Abstract. Mapping target crops before the harvest season for regions lacking crop-specific ground truth is critical for global food security. Utilizing multispectral remote sensing and domain adaptation methods, prior studies strive to produce precise crop maps in these regions (target domain) with the help of the crop-specific labelled remote sensing data from the source regions (source domain). However, existing approaches assume identical label spaces across those domains, a challenge often unmet in reality, necessitating a more adaptable solution. This paper introduces the Multiple Crop Ma

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Song, Zhigang, Daisong Li, Zhongyou Chen, and Wenqin Yang. "Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer." Applied Sciences 13, no. 19 (2023): 11013. http://dx.doi.org/10.3390/app131911013.

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The unsupervised domain-adaptive vehicle re-identification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain; however, there are knowledge differences between the target domain and the source domain. To mitigate domain discrepancies, existing unsupervised domain-adaptive re-identification methods typically require access to source domain data to assist in retraining the target domain model. However, for security reasons, such as data privacy, data exchange between different domains is often infeasible in many scenarios. To this end, this paper propo

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Zhu, Yongchun, Fuzhen Zhuang, and Deqing Wang. "Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5989–96. http://dx.doi.org/10.1609/aaai.v33i01.33015989.

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While Unsupervised Domain Adaptation (UDA) algorithms, i.e., there are only labeled data from source domains, have been actively studied in recent years, most algorithms and theoretical results focus on Single-source Unsupervised Domain Adaptation (SUDA). However, in the practical scenario, labeled data can be typically collected from multiple diverse sources, and they might be different not only from the target domain but also from each other. Thus, domain adapters from multiple sources should not be modeled in the same way. Recent deep learning based Multi-source Unsupervised Domain Adaptati

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Yeung, W. K., and S. Evans. "Time-domain microwave target imaging." IEE Proceedings H Microwaves, Antennas and Propagation 132, no. 6 (1985): 345. http://dx.doi.org/10.1049/ip-h-2.1985.0063.

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Zhong, Jiang, and Zhiying Wang. "MTL-DAS: Automatic Text Summarization for Domain Adaptation." Computational Intelligence and Neuroscience 2022 (June 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/4851828.

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Domain adaptation on text summarization task is always challenging, which is caused by the lack of annotated data in the target domain. Previous methodologies focused more on introducing knowledge in the target domain and shifted the model to the target domain. However, they mostly studied the adaptation to a single low-resource domain, which restricted practicality. In this paper, we propose MTL-DAS, a unified model for multidomain adaptive text summarization, which stands for Multitask Learning for Multidomain Adaptation Summarization model. Combined with BART, we investigate multitask learn

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Lv, Ying, Bofeng Zhang, Guobing Zou, Xiaodong Yue, Zhikang Xu, and Haiyan Li. "Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory." Entropy 24, no. 7 (2022): 966. http://dx.doi.org/10.3390/e24070966.

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Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target doma

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Shu, Yang, Zhangjie Cao, Mingsheng Long, and Jianmin Wang. "Transferable Curriculum for Weakly-Supervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4951–58. http://dx.doi.org/10.1609/aaai.v33i01.33014951.

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Domain adaptation improves a target task by knowledge transfer from a source domain with rich annotations. It is not uncommon that “source-domain engineering” becomes a cumbersome process in domain adaptation: the high-quality source domains highly related to the target domain are hardly available. Thus, weakly-supervised domain adaptation has been introduced to address this difficulty, where we can tolerate the source domain with noises in labels, features, or both. As such, for a particular target task, we simply collect the source domain with coarse labeling or corrupted data. In this paper

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Khatin-Zadeh, Omid, Zahra Eskandari, Yousef Bakhshizadeh-Gashti, Sedigheh Vahdat, and Hassan Banaruee. "An algebraic perspective on abstract and concrete domains." Cognitive Linguistic Studies 6, no. 2 (2019): 354–69. http://dx.doi.org/10.1075/cogls.00036.kha.

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Abstract Looking at isomorphic constructs from an algebraic perspective, this article suggests that every concrete construct is understood by reference to an underlying abstract schema in the mind of comprehender. The complex form of every abstract schema is created by the gradual development of its elementary form. Throughout the process of cognitive development, new features are added to the elementary form of abstract schema, which leads to gradual formation of a fully-developed abstract schema. Every developed abstract schema is the underlying source for understanding an infinite number of

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Cai, Yiqing, Lianggangxu Chen, Haoyue Guan, et al. "Explicit Invariant Feature Induced Cross-Domain Crowd Counting." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 259–67. http://dx.doi.org/10.1609/aaai.v37i1.25098.

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Cross-domain crowd counting has shown progressively improved performance. However, most methods fail to explicitly consider the transferability of different features between source and target domains. In this paper, we propose an innovative explicit Invariant Feature induced Cross-domain Knowledge Transformation framework to address the inconsistent domain-invariant features of different domains. The main idea is to explicitly extract domain-invariant features from both source and target domains, which builds a bridge to transfer more rich knowledge between two domains. The framework consists

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Zhao, Shaoxuan, Xiaoguang Zhou, and Dongyang Hou. "An Anomaly Detection-Based Domain Adaptation Framework for Cross-Domain Building Extraction from Remote Sensing Images." Applied Sciences 13, no. 3 (2023): 1674. http://dx.doi.org/10.3390/app13031674.

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Deep learning-based building extraction methods have achieved a high accuracy in closed remote sensing datasets. In fact, the distribution bias between the source and target domains can lead to a dramatic decrease in their building extraction effect in the target domain. However, the mainstream domain adaptation methods that specifically address this domain bias problem require the reselection of many unlabeled samples and retraining in other target domains. This is time-consuming and laborious and even impossible at small regions. To address this problem, a novel domain adaptation framework f

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Zhang, Huiying, Yongmeng Li, Lei He, Wenbo Zhang, Yuchen Shen, and Lumin Xing. "Class-aware multi-source domain adaptation algorithm for medical image analysis using reweighted matrix matching strategy." PLOS One 20, no. 7 (2025): e0323676. https://doi.org/10.1371/journal.pone.0323676.

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Multi-source domain adaptation leverages complementary knowledge from multiple source domains to enhance transfer effectiveness, making it more suitable for complex medical scenarios compared to single-source domain adaptation. However, most existing studies operate under the assumption that the source and target domains share identical class distributions, leaving the challenge of addressing class shift in multi-source domain adaptation largely unexplored. To address this gap, this study proposes a Class-Aware Multi-Source Domain Adaptation algorithm based on a Reweighted Matrix Matching stra

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Spence, Aaron, and Shaun Bangay. "Domain-Agnostic Representation of Side-Channels." Entropy 26, no. 8 (2024): 684. http://dx.doi.org/10.3390/e26080684.

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Side channels are unintended pathways within target systems that leak internal target information. Side-channel sensing (SCS) is the process of exploiting side channels to extract embedded target information. SCS is well established within the cybersecurity (CYB) domain, and has recently been proposed for medical diagnostics and monitoring (MDM). Remaining unrecognised is its applicability to human–computer interaction (HCI), among other domains (Misc). This article analyses literature demonstrating SCS examples across the MDM, HCI, Misc, and CYB domains. Despite their diversity, established f

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Zhuo, Hankz Hankui, Qiang Yang, Rong Pan, and Lei Li. "Cross-Domain Action-Model Acquisition for Planning via Web Search." Proceedings of the International Conference on Automated Planning and Scheduling 21 (March 22, 2011): 298–305. http://dx.doi.org/10.1609/icaps.v21i1.13449.

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Applying learning techniques to acquire action models is an area of intense research interest. Most previous works in this area have assumed that there is a significant amount of training data available in a planning domain of interest, which we call target domain, where action models are to be learned. However, it is often difficult to acquire sufficient training data to ensure that the learned action models are of high quality. In this paper, we develop a novel approach to learning action models with limited training data in the target domain by transferring knowledge from related auxiliary

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Tang, Shixiang, Peng Su, Dapeng Chen, and Wanli Ouyang. "Gradient Regularized Contrastive Learning for Continual Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (2021): 2665–73. http://dx.doi.org/10.1609/aaai.v35i3.16370.

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Human beings can quickly adapt to environmental changes by leveraging learning experience. However, adapting deep neural networks to dynamic environments by machine learning algorithms remains a challenge. To better understand this issue, we study the problem of continual domain adaptation, where the model is presented with a labelled source domain and a sequence of unlabelled target domains. The obstacles in this problem are both domain shift and catastrophic forgetting. We propose Gradient Regularized Contrastive Learning (GRCL) to solve the obstacles. At the core of our method, gradient reg

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Yoo, Minjong, Woo Kyung Kim, and Honguk Woo. "In-Context Policy Adaptation via Cross-Domain Skill Diffusion." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22191–99. https://doi.org/10.1609/aaai.v39i21.34373.

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In this work, we present an in-context policy adaptation (ICPAD) framework designed for long-horizon multi-task environments, exploring diffusion-based skill learning techniques in cross-domain settings. The framework enables rapid adaptation of skill-based reinforcement learning policies to diverse target domains, especially under stringent constraints on no model updates and only limited target domain data. Specifically, the framework employs a cross-domain skill diffusion scheme, where domain-agnostic prototype skills and a domain-grounded skill adapter are learned jointly and effectively f

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Gu, Yuechun, and Keke Chen. "GAN-Based Domain Inference Attack." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14214–22. http://dx.doi.org/10.1609/aaai.v37i12.26663.

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Model-based attacks can infer training data information from deep neural network models. These attacks heavily depend on the attacker's knowledge of the application domain, e.g., using it to determine the auxiliary data for model-inversion attacks. However, attackers may not know what the model is used for in practice. We propose a generative adversarial network (GAN) based method to explore likely or similar domains of a target model -- the model domain inference (MDI) attack. For a given target (classification) model, we assume that the attacker knows nothing but the input and output formats

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Ni, Zi-Ao, Zixian Su, Xi Yang, Qiufeng Wang, and Kaizhu Huang. "Tight Source Domain Match for Partial Domain Adaptation Based on Maximum Density." Journal of Physics: Conference Series 2278, no. 1 (2022): 012032. http://dx.doi.org/10.1088/1742-6596/2278/1/012032.

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Abstract Unsupervised domain adaptation (UDA) aims at transferring knowledge between a well-labelled ’source domain’ and an unlabelled ’target domain’ by decreasing distribution discrepancy. In real scenario, partial domain adaptation (PDA), where target domain only includes part of the classes of source domain, is adopted as fully-shared label space is often unavailable. Non-identical label spaces across domains lead to performance degradation due to source-unique classes being mis-matched to the target domain, i.e. negative transfer of the target domain. Although existing PDA approaches have

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Ren, Chuan-Xian, Yong-Hui Liu, Xi-Wen Zhang, and Ke-Kun Huang. "Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domain." IEEE Transactions on Image Processing 31 (2022): 2122–35. http://dx.doi.org/10.1109/tip.2022.3152052.

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Zhang, Bo, Xiaoming Zhang, Feiran Huang, and Dezhuang Miao. "Cross-domain knowledge collaboration for blending-target domain adaptation." Information Processing & Management 61, no. 4 (2024): 103730. http://dx.doi.org/10.1016/j.ipm.2024.103730.

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Schäffner, Christina. "Unknown agents in translated political discourse." Target. International Journal of Translation Studies 24, no. 1 (2012): 103–25. http://dx.doi.org/10.1075/target.24.1.07sch.

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This article investigates the role of translation and interpreting in political discourse. It illustrates discursive events in the domain of politics and the resulting discourse types, such as jointly produced texts, press conferences and speeches. It shows that methods of Critical Discourse Analysis can be used effectively to reveal translation and interpreting strategies as well as transformations that occur in recontextualisation processes across languages, cultures, and discourse domains, in particular recontextualisation in mass media. It argues that the complexity of translational activi

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Zheng, Yang, Susanne Schroeder, Georgi K. Kanev, et al. "To Target or Not to Target Schistosoma mansoni Cyclic Nucleotide Phosphodiesterase 4A?" International Journal of Molecular Sciences 24, no. 7 (2023): 6817. http://dx.doi.org/10.3390/ijms24076817.

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Schistosomiasis is a neglected tropical disease with high morbidity. Recently, the Schistosoma mansoni phosphodiesterase SmPDE4A was suggested as a putative new drug target. To support SmPDE4A targeted drug discovery, we cloned, isolated, and biochemically characterized the full-length and catalytic domains of SmPDE4A. The enzymatically active catalytic domain was crystallized in the apo-form (PDB code: 6FG5) and in the cAMP- and AMP-bound states (PDB code: 6EZU). The SmPDE4A catalytic domain resembles human PDE4 more than parasite PDEs because it lacks the parasite PDE-specific P-pocket. Puri

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He, Yuhang, Zhiheng Ma, Xing Wei, Xiaopeng Hong, Wei Ke, and Yihong Gong. "Error-Aware Density Isomorphism Reconstruction for Unsupervised Cross-Domain Crowd Counting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1540–48. http://dx.doi.org/10.1609/aaai.v35i2.16245.

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This paper focuses on the unsupervised domain adaptation problem for video-based crowd counting, in which we use labeled data as source domain and unlabelled video data as target domain. It is challenging as there is a huge gap between the source and the target domain and no annotations of samples are available in the target domain. The key issue is how to utilize unlabelled videos in the target domain for knowledge learning and transferring from the source domain. To tackle this problem, we propose a novel Error-aware Density Isomorphism REConstruction Network (EDIREC-Net) for cross-domain cr

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Wittich, D. "DEEP DOMAIN ADAPTATION BY WEIGHTED ENTROPY MINIMIZATION FOR THE CLASSIFICATION OF AERIAL IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 591–98. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-591-2020.

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Abstract. Fully convolutional neural networks (FCN) are successfully used for the automated pixel-wise classification of aerial images and possibly additional data. However, they require many labelled training samples to perform well. One approach addressing this issue is semi-supervised domain adaptation (SSDA). Here, labelled training samples from a source domain and unlabelled samples from a target domain are used jointly to obtain a target domain classifier, without requiring any labelled samples from the target domain. In this paper, a two-step approach for SSDA is proposed. The first ste

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