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Статті в журналах з теми "Domain Adversarial Learning"
Rosenberg, Ishai, Asaf Shabtai, Yuval Elovici, and Lior Rokach. "Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain." ACM Computing Surveys 54, no. 5 (June 2021): 1–36. http://dx.doi.org/10.1145/3453158.
Повний текст джерелаXu, Minghao, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang. "Adversarial Domain Adaptation with Domain Mixup." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6502–9. http://dx.doi.org/10.1609/aaai.v34i04.6123.
Повний текст джерелаWu, Lan, Chongyang Li, Qiliang Chen, and Binquan Li. "Deep adversarial domain adaptation network." International Journal of Advanced Robotic Systems 17, no. 5 (September 1, 2020): 172988142096464. http://dx.doi.org/10.1177/1729881420964648.
Повний текст джерелаZhou, Kaiyang, Yongxin Yang, Timothy Hospedales, and Tao Xiang. "Deep Domain-Adversarial Image Generation for Domain Generalisation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 13025–32. http://dx.doi.org/10.1609/aaai.v34i07.7003.
Повний текст джерелаChen, 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 (April 3, 2020): 3521–28. http://dx.doi.org/10.1609/aaai.v34i04.5757.
Повний текст джерелаWu, Yuan, and Yuhong Guo. "Dual Adversarial Co-Learning for Multi-Domain Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6438–45. http://dx.doi.org/10.1609/aaai.v34i04.6115.
Повний текст джерелаZou, Han, Yuxun Zhou, Jianfei Yang, Huihan Liu, Hari Prasanna Das, and Costas J. Spanos. "Consensus Adversarial Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5997–6004. http://dx.doi.org/10.1609/aaai.v33i01.33015997.
Повний текст джерелаTang, Hui, and Kui Jia. "Discriminative Adversarial Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5940–47. http://dx.doi.org/10.1609/aaai.v34i04.6054.
Повний текст джерелаLi, Wenjing, and Zhongcheng Wu. "OVL: One-View Learning for Human Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11410–17. http://dx.doi.org/10.1609/aaai.v34i07.6804.
Повний текст джерелаNguyen Duc, Tho, Chanh Minh Tran, Phan Xuan Tan, and Eiji Kamioka. "Domain Adaptation for Imitation Learning Using Generative Adversarial Network." Sensors 21, no. 14 (July 9, 2021): 4718. http://dx.doi.org/10.3390/s21144718.
Повний текст джерелаДисертації з теми "Domain Adversarial Learning"
Bejiga, Mesay Belete. "Adversarial approaches to remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257100.
Повний текст джерелаRahman, Mohammad Mahfujur. "Deep domain adaptation and generalisation." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205619/1/Mohammad%20Mahfujur_Rahman_Thesis.pdf.
Повний текст джерелаGustafsson, Fredrik, and Erik Linder-Norén. "Automotive 3D Object Detection Without Target Domain Annotations." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148585.
Повний текст джерелаBrandt, Carl-Simon, Jonathan Kleivard, and Andreas Turesson. "Convolutional, adversarial and random forest-based DGA detection : Comparative study for DGA detection with different machine learning algorithms." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20103.
Повний текст джерелаMarzinotto, Gabriel. "Semantic frame based analysis using machine learning techniques : improving the cross-domain generalization of semantic parsers." Electronic Thesis or Diss., Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0483.
Повний текст джерелаMaking semantic parsers robust to lexical and stylistic variations is a real challenge with many industrial applications. Nowadays, semantic parsing requires the usage of domain-specific training corpora to ensure acceptable performances on a given domain. Transfer learning techniques are widely studied and adopted when addressing this lack of robustness, and the most common strategy is the usage of pre-trained word representations. However, the best parsers still show significant performance degradation under domain shift, evidencing the need for supplementary transfer learning strategies to achieve robustness. This work proposes a new benchmark to study the domain dependence problem in semantic parsing. We use this bench to evaluate classical transfer learning techniques and to propose and evaluate new techniques based on adversarial learning. All these techniques are tested on state-of-the-art semantic parsers. We claim that adversarial learning approaches can improve the generalization capacities of models. We test this hypothesis on different semantic representation schemes, languages and corpora, providing experimental results to support our hypothesis
Ackerman, Wesley. "Semantic-Driven Unsupervised Image-to-Image Translation for Distinct Image Domains." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8684.
Повний текст джерелаTsai, Jen-Chieh, and 蔡仁傑. "Deep Adversarial Learning and Domain Adaptation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/3848u8.
Повний текст джерела國立交通大學
電機工程學系
105
Deep learning has been rapidly developing from different aspects of theories and applications where a large amount of labeled data are available for supervised training. However, in practice, it is time-consuming to collect a large set of labeled data. In real world, we may only observe a limited set of labeled data and unlabeled data. How to perform data augmentation and improve model regularization is a crucial research topic. Recently, adversarial learning has been discovering to generate or synthesize realistic data without the mixing problem in traditional model based on Markov chain. This thesis deals with the generation of new training samples based on deep adversarial learning. Our goal is to carry out the adversarial generation of new samples and apply it for defect classification in manufacturing process. To improve system performance, we introduce the additional latent codes and maximize the mutual information between generative samples and latent codes to build a conditional generative adversarial model. This model is capable of generating a variety of samples under the same class. We evaluate the performance of this unsupervised model by detecting the defect conditions in production process of copper foil images. On the other hand, transfer learning provides an alternative method to handle the problem of insufficient labeled data where data generation is not required. Transfer learning involves several issues owing to different setups. The issue we concern is mainly on domain adaptation. Domain adaptation aims to adapt a model from source domain to target domain through learning the shared representation that allows knowledge transfer across domains. Traditional domain adaptation methods are specialized to learn the shared representation for distribution matching between source domain and target domain where the individual information in both domains is missing. In this thesis, we present a deep hybrid adversarial learning framework which captures the shared information and the individual information simultaneously. Our idea is to estimate the shared feature which is informative for classification and the individual feature which contains the domain specific information. We use adaptation network to extract the shared feature and separation network to extract individual feature. Both adaptation and separation network are seen as an adversarial network. A hybrid adversarial learning is incorporated in the separation network as well as the adaptation network where the according to the minimax optimization over separation loss and domain discrepancy, respectively. The idea in the adaptation network is that we want to extract shared feature that an optimal discriminator cannot tell where feature come from. The idea in the separation network is that we want to extract feature including shared and individual feature which can be separated even by a bad discriminator. In other words the features have to be good enough to force the discriminator to classify them correctly. For the experiment on generative adversarial model, we investigate different unsupervised learning methods for defect detection in presence of copper foil images. In general, defect detection requires very high accuracy but the defect rate usually is relatively low which means the images with and without defect are very unbalanced. We generate the defective images to balance the training data between defective images and non-defective images conditioned on different classes. For the experiments on domain adaptation problem, we evaluate the proposed method on different tasks and show the merit of using the proposed adversarial domain separation and adaptation in the tasks of sentiment classification and image recognition.
Wei, Kai-Ya, and 魏凱亞. "Generative Adversarial Guided Learning for Domain Adaptation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/zt2car.
Повний текст джерела國立清華大學
資訊工程學系所
106
This thesis focuses on unsupervised domain adaptation problem, which aims to learn a classification model on an unlabelled target domain by referring to a fully-labelled source domain. Our goal is twofold: bridging the gap between source-target domains, and deriving a discriminative model for the target domain. We propose a Generative Adversarial Guided Learning (GAGL) model to tackle the task. To minimize the source-target domain shift, we adopt the idea of domain adversarial training to build a classification network. Next, to derive a target discriminative classifier, we propose to include a generative network to guide the classifier so as to push its decision boundaries away from high density area of target domain. The proposed GAGL model is an end-to-end framework and thus can simultaneously learn the classification model and refine its decision boundary under the guidance of the generator. Our experimental results show that the proposed GAGL model not only outperforms the baseline domain adversarial model but also achieves competitive results with state-of-the-art methods on standard benchmarks.
Pereira, João Afonso Pinto. "Fingerprint Anti Spoofing - Domain Adaptation and Adversarial Learning." Master's thesis, 2020. https://hdl.handle.net/10216/128390.
Повний текст джерелаPereira, João Afonso Pinto. "Fingerprint Anti Spoofing - Domain Adaptation and Adversarial Learning." Dissertação, 2020. https://hdl.handle.net/10216/128390.
Повний текст джерелаЧастини книг з теми "Domain Adversarial Learning"
Lu, Weikai, Jian Chen, Hao Zheng, Haoyi Fan, Eng Yee Wei, Xinrong Cao, and Deyang Zhang. "Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis." In Machine Learning for Cyber Security, 436–46. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20099-1_37.
Повний текст джерелаChen, Qian, Yuntao Du, Zhiwen Tan, Yi Zhang, and Chongjun Wang. "Unsupervised Domain Adaptation with Joint Domain-Adversarial Reconstruction Networks." In Machine Learning and Knowledge Discovery in Databases, 640–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67661-2_38.
Повний текст джерелаGuo, Zuwei, Nahid UI Islam, Michael B. Gotway, and Jianming Liang. "Discriminative, Restorative, and Adversarial Learning: Stepwise Incremental Pretraining." In Domain Adaptation and Representation Transfer, 66–76. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16852-9_7.
Повний текст джерелаWang, Jinghua, and Jianmin Jiang. "Adversarial Learning for Zero-Shot Domain Adaptation." In Computer Vision – ECCV 2020, 329–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58589-1_20.
Повний текст джерелаZhang, Youshan, and Brian D. Davison. "Adversarial Continuous Learning in Unsupervised Domain Adaptation." In Pattern Recognition. ICPR International Workshops and Challenges, 672–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68790-8_52.
Повний текст джерелаSaito, Kuniaki, Shohei Yamamoto, Yoshitaka Ushiku, and Tatsuya Harada. "Adversarial Learning Approach for Open Set Domain Adaptation." In Domain Adaptation in Computer Vision with Deep Learning, 175–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45529-3_10.
Повний текст джерелаHu, Lanqing, Meina Kan, Shiguang Shan, and Xilin Chen. "Unsupervised Domain Adaptation with Duplex Generative Adversarial Network." In Domain Adaptation in Computer Vision with Deep Learning, 97–116. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45529-3_6.
Повний текст джерелаWu, Yuan, Diana Inkpen, and Ahmed El-Roby. "Dual Mixup Regularized Learning for Adversarial Domain Adaptation." In Computer Vision – ECCV 2020, 540–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58526-6_32.
Повний текст джерелаMemmel, Marius, Camila Gonzalez, and Anirban Mukhopadhyay. "Adversarial Continual Learning for Multi-domain Hippocampal Segmentation." In Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health, 35–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87722-4_4.
Повний текст джерелаGrießhaber, Daniel, Ngoc Thang Vu, and Johannes Maucher. "Low-Resource Text Classification Using Domain-Adversarial Learning." In Statistical Language and Speech Processing, 129–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00810-9_12.
Повний текст джерелаТези доповідей конференцій з теми "Domain Adversarial Learning"
Zhang, Zhifeng, Xuejing Kang, and Anlong Ming. "Domain Adversarial Learning for Color Constancy." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/236.
Повний текст джерелаRaab, Christoph, Sascha Saralajew, and Frank-Michael Schleif. "Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation." In ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2021. http://dx.doi.org/10.14428/esann/2021.es2021-103.
Повний текст джерелаChien, Jen-Tzung, and Ching-Wei Huang. "Stochastic Adversarial Learning for Domain Adaptation." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207478.
Повний текст джерелаLi, Haoliang, Sinno Jialin Pan, Shiqi Wang, and Alex C. Kot. "Domain Generalization with Adversarial Feature Learning." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00566.
Повний текст джерелаTsai, Jen-Chieh, and Jen-Tzung Chien. "Adversarial domain separation and adaptation." In 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2017. http://dx.doi.org/10.1109/mlsp.2017.8168121.
Повний текст джерелаJiang, Pin, Aming Wu, Yahong Han, Yunfeng Shao, Meiyu Qi, and Bingshuai Li. "Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/130.
Повний текст джерелаOsahor, Uche, and Nasser Nasrabadi. "Deep adversarial attack on target detection systems." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, edited by Tien Pham. SPIE, 2019. http://dx.doi.org/10.1117/12.2518970.
Повний текст джерелаYang, Pei, Qi Tan, Jieping Ye, Hanghang Tong, and Jingrui He. "Deep Multi-Task Learning with Adversarial-and-Cooperative Nets." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/566.
Повний текст джерелаSinatra, Taylor, Cristina Comaniciu, Myron Hohil, and Thomas A. LaPeruta. "Cooperative monitoring for detecting adversarial communication." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, edited by Tien Pham, Latasha Solomon, and Myron E. Hohil. SPIE, 2021. http://dx.doi.org/10.1117/12.2588038.
Повний текст джерелаYou, Suya, and C.-C. Jay Kuo. "Defending against adversarial attacks in deep neural networks." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, edited by Tien Pham. SPIE, 2019. http://dx.doi.org/10.1117/12.2519268.
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