improving adversarial discriminative domain adaptation
Few-shot Supervised Domain Adaptation. Type-Supervised Domain Adaptation for Joint Segmentation and POS-Tagging. Jingjing Li, Erpeng Chen, Zhengming Ding, Lei Zhu, Ke Lu, Heng Tao Shen. "Answer Again: Improving VQA with Cascaded-Answering Model". FADA: Few-Shot Adversarial Domain Adaptation (2017) Augmented-Cyc: Augmented Cyclic Adversarial Learning for Domain Adaptation (2018) Embedding methods. We respect The General Data Protection Regulation 2016/679. A generative adversarial network consists of two neural networks pitted against each other. Domain Adaptation : Chair: Albarqouni, Shadi: Helmholtz Center Munich: Co-Chair: Hang Dai, Hang: Mohamed Bin Zayed University of Artificial Intelligence : 15:15-15:21, Paper ThC2.1: Add to My Program : Spatial Decomposition for Robust Domain Adaptation in Prostate Cancer Detection Competency Evaluation in Voice Mimicking Using Acoustic Cues 2014.04, pp. Liang Peng, Yang Yang, Xiaopeng Zhang, Yanli Ji, Huimin Lu and Heng Tao Shen. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020. The generative network G is tasked with creating samples that the discriminative network D is supposed to classify as coming from the generative network or the training data. Area Chair: NeurIPS 2021, ICCV 2021, AAAI 2021, BMVC 2020. Competency Evaluation in Voice Mimicking Using Acoustic Cues Each dialogue is labeled with comprehensive dialogue annotations, including dialogue goal in the form of natural language description, domain, dialogue states and acts at both the user and system side. He received the B.Eng. The lab aims to provide efficient algorithm support and secure, reliable, and powerful computing engines for various industries and … A Robust Adversarial Training Approach to Machine Reading Comprehension. focused on domain adaptation to improve model 's overall transferability. Domain Adaptation with Reconstruction for Disaster Tweet Classification Xukun Li: Department of Computer Science, Kansas State University; Doina Caragea: Department of … Mengmeng Jing, Jidong Zhao, Jingjing Li, Lei Zhu, Yang Yang and Heng Tao Shen. [89] Jinsong Su +, Jiali Zeng +, Jun Xie, Huating Wen, Yongjing Yin and Yang Liu. Self-Supervised Pre-Training with Acoustic Configurations for Replay Spoofing Detection Hye-jin Shim, Hee-Soo Heo, Jee-weon Jung, Ha-Jin Yu . 30. Our solution is conceptually simple, and not relying on a specific parser architecture, making it applicable to the current best-performing parsers. Improving Adversarial Discriminative Domain Adaptation [arXiv 10 Sep 2018] M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning [arXiv 6 Jul 2018] [Pytorch(official)] Factorized Adversarial Networks for Unsupervised Domain Adaptation [arXiv 4 Jun 2018] DiDA: Disentangled Synthesis for Domain Adaptation [arXiv 21 May 2018] In Proceedings of the 14th Conference of the European Chapter of the ACL (EACL 2014). “Maximum Density Divergence for Domain Adaptation”. A Robust Adversarial Training Approach to Machine Reading Comprehension. “MRA-Net: Improving VQA via Multi-modal Relation Attention Network”. Datasets are an integral part of the field of machine learning. In Proceedings of the 14th Conference of the European Chapter of the ACL (EACL 2014). 2019. focused on domain adaptation to improve model 's overall transferability. Only a few target examples are available, but they are labelled. Liang Peng, Yang Yang, Zheng Wang, Zi Huang, Heng Tao Shen. Few-shot Supervised Domain Adaptation. Self-Supervised Pre-Training with Acoustic Configurations for Replay Spoofing Detection Hye-jin Shim, Hee-Soo Heo, Jee-weon Jung, Ha-Jin Yu . and PhD degrees from University of Science and Technology of China, in 2001 and 2005, respectively. Associate Editor: Computer Graphics Forum (2021-2023), IET Computer Vision (2020-2022).. Mentor: LatinX in AI Mentoring Program (@CVPR 2021). 588-597， Gothenburg, Sweden.data. 作为计算机视觉领域三大顶会之一，cvpr2021目前已公布了所有接收论文id，一共有1663篇论文被接收，接收率为23.7%，虽然接受率相比去年有所上升，但竞争也是非常激烈，相关报道： cvpr 2021接收结果出炉！录用1663… “MRA-Net: Improving VQA via Multi-modal Relation Attention Network”. Both single- and multi-domain dialogues are constructed, accounting for 65% and 35%, respectively. Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation Pin Jiang, Aming Wu, Yahong Han, Yunfeng Shao, Meiyu Qi, Bingshuai Li Main track (Computer Vision) Bottom-up and Top-down: Bidirectional Additive Net for Edge Detection Lianli Gao, Zhilong Zhou, Heng Tao Shen, Jingkuan Song Exploring Discriminative Word-Level Domain Contexts for Multi-domain … IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020. “MRA-Net: Improving VQA via Multi-modal Relation Attention Network”. The only requirement I used for selecting papers for this list is that it is primarily a paper about adversarial examples, or extensively uses adversarial examples. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group-level features through adversarial learning. The Web Conference is announcing latest news and developments biweekly or on a monthly basis. While learning from simulations is a particular instance of domain adaptation, it is worth outlining some other examples of domain adaptation. (CCF A类). The realization of automatic product recognition has great significance for both economic and social progress because it is more reliable than manual operation and time-saving. "Answer Again: Improving VQA with Cascaded-Answering Model". SIGGRAPH Technical Program Committee: SIGGRAPH Asia 2020, SIGGRAPH Asia 2021.. Reviewer: CVPR 2021, ICLR 2021, ICML 2021.. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. Competency Evaluation in Voice Mimicking Using Acoustic Cues Domain Adaptation with Reconstruction for Disaster Tweet Classification Xukun Li: Department of Computer Science, Kansas State University; Doina Caragea: Department of … His research interests include (multi-agent) reinforcement learning, deep learning and data science with various real-world applications of recommender systems, search engines, text mining & generation, knowledge graphs, game AI etc. These datasets are applied for machine-learning research and have been cited in peer-reviewed academic journals. "Adaptive Component Embedding for Domain Adaptation". Research Group "Adaptive Component Embedding for Domain Adaptation". The Web Conference is announcing latest news and developments biweekly or on a monthly basis. Domain Adaptation : Chair: Albarqouni, Shadi: Helmholtz Center Munich: Co-Chair: Hang Dai, Hang: Mohamed Bin Zayed University of Artificial Intelligence : 15:15-15:21, Paper ThC2.1: Add to My Program : Spatial Decomposition for Robust Domain Adaptation in Prostate Cancer Detection Both single- and multi-domain dialogues are constructed, accounting for 65% and 35%, respectively. Datasets are an integral part of the field of machine learning. Performing groundbreaking Natural Language Processing research since 1999. While pursuing the PhD degree, he worked Unsupervised Domain Adaptation Unsupervised do-main adaptation (UDA) refers to the scenario where no labels are available for the target domain. Weinan Zhang is now a tenure-track associate professor at Shanghai Jiao Tong University. A generative adversarial network consists of two neural networks pitted against each other. Each dialogue is labeled with comprehensive dialogue annotations, including dialogue goal in the form of natural language description, domain, dialogue states and acts at both the user and system side. Hoffman et al. Improving Adversarial Discriminative Domain Adaptation [arXiv 10 Sep 2018] M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning [arXiv 6 Jul 2018] [Pytorch(official)] Factorized Adversarial Networks for Unsupervised Domain Adaptation [arXiv 4 Jun 2018] DiDA: Disentangled Synthesis for Domain Adaptation [arXiv 21 May 2018] SIGGRAPH Technical Program Committee: SIGGRAPH Asia 2020, SIGGRAPH Asia 2021.. Reviewer: CVPR 2021, ICLR 2021, ICML 2021.. We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. The generative network G is tasked with creating samples that the discriminative network D is supposed to classify as coming from the generative network or the training data. @InProceedings{Raab_2020_ACCV, author = {Raab, Christoph and Vath, Philipp and Meier, Peter and Schleif, Frank-Michael}, title = {Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {November}, year = {2020} } Performing groundbreaking Natural Language Processing research since 1999. In Proceedings of AAAI2020. The generative network G is tasked with creating samples that the discriminative network D is supposed to classify as coming from the generative network or the training data. A generative adversarial network consists of two neural networks pitted against each other. Jingjing Li, Erpeng Chen, Zhengming Ding, Lei Zhu, Ke Lu, Heng Tao Shen. "Answer Again: Improving VQA with Cascaded-Answering Model". Exploring Discriminative Word-Level Domain Contexts for Multi-domain … Mengmeng Jing, Jidong Zhao, Jingjing Li, Lei Zhu, Yang Yang and Heng Tao Shen. 2014.04, pp. (2018) proposed a novel discriminatively trained cycle-consistent adversarial domain adaptation model that seeks to reduce the domain shift by transferring source images to the target style with a cycle consistency loss and then aligning the cross-domain feature distributions of the task network through adversarial training. A Robust Adversarial Training Approach to Machine Reading Comprehension. Research Group 30. Recent Services. Dong Xu is Chair in Computer Engineering and ARC Future Fellow at the School of Electrical and Information Engineering, The University of Sydney, Australia. Liang Peng, Yang Yang, Zheng Wang, Zi Huang, Heng Tao Shen. While domain adaptation approaches can help to address the out-of-domain robustness problem (Pan and Yang,2010;Zhang et al.,2019;Ma et al.,2019), they rely on the avail-ability of either labeled data or at least a target corpus which is usually not available at training While learning from simulations is a particular instance of domain adaptation, it is worth outlining some other examples of domain adaptation. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group-level features through adversarial learning. Associate Editor: Computer Graphics Forum (2021-2023), IET Computer Vision (2020-2022).. Mentor: LatinX in AI Mentoring Program (@CVPR 2021). IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020. ... 42 【域适应】Gradually Vanishing Bridge for Adversarial Domain Adaptation. Dong Xu is Chair in Computer Engineering and ARC Future Fellow at the School of Electrical and Information Engineering, The University of Sydney, Australia. While domain adaptation approaches can help to address the out-of-domain robustness problem (Pan and Yang,2010;Zhang et al.,2019;Ma et al.,2019), they rely on the avail-ability of either labeled data or at least a target corpus which is usually not available at training These datasets are applied for machine-learning research and have been cited in peer-reviewed academic journals. In Proceedings of the 14th Conference of the European Chapter of the ACL (EACL 2014). Dual-Adversarial Domain Adaptation for Generalized Replay Attack Detection Hongji Wang, Heinrich Dinkel, Shuai Wang, Yanmin Qian, Kai Yu . and PhD degrees from University of Science and Technology of China, in 2001 and 2005, respectively. Biography Jiebo Luo joined the University of Rochester in Fall 2011 after over fifteen prolific years at Kodak Research Laboratories, where he was a Senior Principal Scientist leading research and advanced development.He has been involved in numerous technical conferences, including serving as the program co-chair of ACM Multimedia 2010, IEEE CVPR 2012 and IEEE ICIP 2017. (CCF A类). and PhD degrees from University of Science and Technology of China, in 2001 and 2005, respectively. “Maximum Density Divergence for Domain Adaptation”. Due to the sheer quantity of papers, I can't guarantee that I actually have found all of them. Domain Adaptation : Chair: Albarqouni, Shadi: Helmholtz Center Munich: Co-Chair: Hang Dai, Hang: Mohamed Bin Zayed University of Artificial Intelligence : 15:15-15:21, Paper ThC2.1: Add to My Program : Spatial Decomposition for Robust Domain Adaptation in Prostate Cancer Detection These datasets are applied for machine-learning research and have been cited in peer-reviewed academic journals. Weinan Zhang is now a tenure-track associate professor at Shanghai Jiao Tong University. Jingjing Li, Erpeng Chen, Zhengming Ding, Lei Zhu, Ke Lu, Heng Tao Shen. Only a few target examples are available, but they are labelled. Type-Supervised Domain Adaptation for Joint Segmentation and POS-Tagging. Performing groundbreaking Natural Language Processing research since 1999. cvpr2021 最全整理：论文分类汇总 / 代码 / 项目 / 论文解读（更新中）【计算机视觉】,极市视觉算法开发者社区,旨在为视觉算法开发者提供高质量视觉前沿学术理论,技术干货分享,结识同业伙伴,协同翻译国外视觉算法干货,分享视觉算法应用的平台 "Adaptive Component Embedding for Domain Adaptation". Dual-Adversarial Domain Adaptation for Generalized Replay Attack Detection Hongji Wang, Heinrich Dinkel, Shuai Wang, Yanmin Qian, Kai Yu . (CCF A类). Type-Supervised Domain Adaptation for Joint Segmentation and POS-Tagging. cvpr2021 最全整理：论文分类汇总 / 代码 / 项目 / 论文解读（更新中）【计算机视觉】,极市视觉算法开发者社区,旨在为视觉算法开发者提供高质量视觉前沿学术理论,技术干货分享,结识同业伙伴,协同翻译国外视觉算法干货,分享视觉算法应用的平台 We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. Improving Adversarial Discriminative Domain Adaptation [arXiv 10 Sep 2018] M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning [arXiv 6 Jul 2018] [Pytorch(official)] Factorized Adversarial Networks for Unsupervised Domain Adaptation [arXiv 4 Jun 2018] DiDA: Disentangled Synthesis for Domain Adaptation [arXiv 21 May 2018] IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. The realization of automatic product recognition has great significance for both economic and social progress because it is more reliable than manual operation and time-saving. Mengmeng Jing, Jidong Zhao, Jingjing Li, Lei Zhu, Yang Yang and Heng Tao Shen. (2018) proposed a novel discriminatively trained cycle-consistent adversarial domain adaptation model that seeks to reduce the domain shift by transferring source images to the target style with a cycle consistency loss and then aligning the cross-domain feature distributions of the task network through adversarial training. 31. The realization of automatic product recognition has great significance for both economic and social progress because it is more reliable than manual operation and time-saving. “Maximum Density Divergence for Domain Adaptation”. Our solution is conceptually simple, and not relying on a specific parser architecture, making it applicable to the current best-performing parsers. Adversarial methods. Liang Peng, Yang Yang, Zheng Wang, Zi Huang, Heng Tao Shen. The lab aims to provide efficient algorithm support and secure, reliable, and powerful computing engines for various industries and … Exploring Discriminative Word-Level Domain Contexts for Multi-domain … The only requirement I used for selecting papers for this list is that it is primarily a paper about adversarial examples, or extensively uses adversarial examples. 作为计算机视觉领域三大顶会之一，cvpr2021目前已公布了所有接收论文id，一共有1663篇论文被接收，接收率为23.7%，虽然接受率相比去年有所上升，但竞争也是非常激烈，相关报道： cvpr 2021接收结果出炉！录用1663… Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation Jinyu Yang1, Chunyuan Li1, Weizhi An1, Hehuan Ma1, Yuzhi Guo1, Yu Rong2, Peilin Zhao2, Junzhou Huang1 1University of Texas at Arlington 2Tencent AI Lab Abstract Recent studies imply that deep neural networks are vulner- His research interests include (multi-agent) reinforcement learning, deep learning and data science with various real-world applications of recommender systems, search engines, text mining & generation, knowledge graphs, game AI etc. Taking time to identify expected products and waiting for the checkout in a retail store are common scenes we all encounter in our daily lives. 30. Biography Jiebo Luo joined the University of Rochester in Fall 2011 after over fifteen prolific years at Kodak Research Laboratories, where he was a Senior Principal Scientist leading research and advanced development.He has been involved in numerous technical conferences, including serving as the program co-chair of ACM Multimedia 2010, IEEE CVPR 2012 and IEEE ICIP 2017. 2019. Few-shot Supervised Domain Adaptation. 2019. The only requirement I used for selecting papers for this list is that it is primarily a paper about adversarial examples, or extensively uses adversarial examples. 588-597， Gothenburg, Sweden.data. The Data Analytics and Intelligence Lab is committed to the research and development of next-generation systems and algorithms for the distributed storage, data management, query processing, analytics, and machine learning on massive and heterogeneous data. In Proceedings of AAAI2020. Liang Peng, Yang Yang, Xiaopeng Zhang, Yanli Ji, Huimin Lu and Heng Tao Shen. We respect The General Data Protection Regulation 2016/679. 2013; Meishan Zhang, Yue Zhang, Wanxiang Che and Ting Liu. FADA: Few-Shot Adversarial Domain Adaptation (2017) Augmented-Cyc: Augmented Cyclic Adversarial Learning for Domain Adaptation (2018) Embedding methods. FADA: Few-Shot Adversarial Domain Adaptation (2017) Augmented-Cyc: Augmented Cyclic Adversarial Learning for Domain Adaptation (2018) Embedding methods. The Data Analytics and Intelligence Lab is committed to the research and development of next-generation systems and algorithms for the distributed storage, data management, query processing, analytics, and machine learning on massive and heterogeneous data. Due to the sheer quantity of papers, I can't guarantee that I actually have found all of them. Only a few target examples are available, but they are labelled. Hoffman et al. Datasets are an integral part of the field of machine learning. (2018) proposed a novel discriminatively trained cycle-consistent adversarial domain adaptation model that seeks to reduce the domain shift by transferring source images to the target style with a cycle consistency loss and then aligning the cross-domain feature distributions of the task network through adversarial training. Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation Pin Jiang, Aming Wu, Yahong Han, Yunfeng Shao, Meiyu Qi, Bingshuai Li Main track (Computer Vision) Bottom-up and Top-down: Bidirectional Additive Net for Edge Detection Lianli Gao, Zhilong Zhou, Heng Tao Shen, Jingkuan Song Taking time to identify expected products and waiting for the checkout in a retail store are common scenes we all encounter in our daily lives. Both single- and multi-domain dialogues are constructed, accounting for 65% and 35%, respectively. Adversarial methods. 16 【半监督学习】Regularizing Discriminative Capability of CGANs for Semi-Supervised Generative Learning. Chinese Parsing Exploiting Characters. Dong Xu is Chair in Computer Engineering and ARC Future Fellow at the School of Electrical and Information Engineering, The University of Sydney, Australia. focused on domain adaptation to improve model 's overall transferability. The lab aims to provide efficient algorithm support and secure, reliable, and powerful computing engines for various industries and … He received the B.Eng. While domain adaptation approaches can help to address the out-of-domain robustness problem (Pan and Yang,2010;Zhang et al.,2019;Ma et al.,2019), they rely on the avail-ability of either labeled data or at least a target corpus which is usually not available at training His research interests include (multi-agent) reinforcement learning, deep learning and data science with various real-world applications of recommender systems, search engines, text mining & generation, knowledge graphs, game AI etc. Recent Services. cvpr2021 最全整理：论文分类汇总 / 代码 / 项目 / 论文解读（更新中）【计算机视觉】,极市视觉算法开发者社区,旨在为视觉算法开发者提供高质量视觉前沿学术理论,技术干货分享,结识同业伙伴,协同翻译国外视觉算法干货,分享视觉算法应用的平台 Dual-Adversarial Domain Adaptation for Generalized Replay Attack Detection Hongji Wang, Heinrich Dinkel, Shuai Wang, Yanmin Qian, Kai Yu . 2014.04, pp. While pursuing the PhD degree, he worked Biography Jiebo Luo joined the University of Rochester in Fall 2011 after over fifteen prolific years at Kodak Research Laboratories, where he was a Senior Principal Scientist leading research and advanced development.He has been involved in numerous technical conferences, including serving as the program co-chair of ACM Multimedia 2010, IEEE CVPR 2012 and IEEE ICIP 2017. Liang Peng, Yang Yang, Xiaopeng Zhang, Yanli Ji, Huimin Lu and Heng Tao Shen. Area Chair: NeurIPS 2021, ICCV 2021, AAAI 2021, BMVC 2020. Each dialogue is labeled with comprehensive dialogue annotations, including dialogue goal in the form of natural language description, domain, dialogue states and acts at both the user and system side. Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation Pin Jiang, Aming Wu, Yahong Han, Yunfeng Shao, Meiyu Qi, Bingshuai Li Main track (Computer Vision) Bottom-up and Top-down: Bidirectional Additive Net for Edge Detection Lianli Gao, Zhilong Zhou, Heng Tao Shen, Jingkuan Song 作为计算机视觉领域三大顶会之一，cvpr2021目前已公布了所有接收论文id，一共有1663篇论文被接收，接收率为23.7%，虽然接受率相比去年有所上升，但竞争也是非常激烈，相关报道： cvpr 2021接收结果出炉！录用1663… IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020. Taking time to identify expected products and waiting for the checkout in a retail store are common scenes we all encounter in our daily lives. Weinan Zhang is now a tenure-track associate professor at Shanghai Jiao Tong University. 16 【半监督学习】Regularizing Discriminative Capability of CGANs for Semi-Supervised Generative Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020. Domain Adaptation with Reconstruction for Disaster Tweet Classification Xukun Li: Department of Computer Science, Kansas State University; Doina Caragea: Department of … Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation Jinyu Yang1, Chunyuan Li1, Weizhi An1, Hehuan Ma1, Yuzhi Guo1, Yu Rong2, Peilin Zhao2, Junzhou Huang1 1University of Texas at Arlington 2Tencent AI Lab Abstract Recent studies imply that deep neural networks are vulner-
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