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检索条件"机构=School of Electrical and Computer Engineering Center for Signal and Image Processing"
411 条 记 录,以下是11-20 订阅
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Taxes are All You Need: Integration Of Taxonomical Hierarchy Relationships Into the Contrastive Loss
Taxes are All You Need: Integration Of Taxonomical Hierarchy...
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IEEE International Conference on image processing
作者: Kiran Kokilepersaud Yavuz Yarici Mohit Prabhushankar Ghassan AlRegib OLIVES at the Center for Signal and Information Processing CSIP School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA
In this work, we propose a novel supervised contrastive loss that enables the integration of taxonomic hierarchy information during the representation learning process. A supervised contrastive loss operates by enforc... 详细信息
来源: 评论
Targeting Negative Flips in Active Learning using Validation Sets
Targeting Negative Flips in Active Learning using Validation...
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IEEE International Conference on Big Data
作者: Ryan Benkert Mohit Prabhushankar Ghassan AlRegib OLIVES at the Center for Signal and Information Processing CSIP School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA
The performance of active learning algorithms can be improved in two ways. The often used and intuitive way is by reducing the overall error rate within the test set. The second way is to ensure that correct predictio... 详细信息
来源: 评论
Targeting Negative Flips in Active Learning using Validation Sets
arXiv
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arXiv 2024年
作者: Benkert, Ryan Prabhushankar, Mohit AlRegib, Ghassan OLIVES The Center for Signal and Information Processing CSIP School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States
The performance of active learning algorithms can be improved in two ways. The often used and intuitive way is by reducing the overall error rate within the test set. The second way is to ensure that correct predictio... 详细信息
来源: 评论
Taxes Are All You Need: Integration of Taxonomical Hierarchy Relationships into the Contrastive Loss
arXiv
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arXiv 2024年
作者: Kokilepersaud, Kiran Yarici, Yavuz Prabhushankar, Mohit AlRegib, Ghassan OLIVES The Center for Signal and Information Processing CSIP School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States
In this work, we propose a novel supervised contrastive loss that enables the integration of taxonomic hierarchy information during the representation learning process. A supervised contrastive loss operates by enforc... 详细信息
来源: 评论
Explaining Representation Learning with Perceptual Components
arXiv
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arXiv 2024年
作者: Yarici, Yavuz Kokilepersaud, Kiran Prabhushankar, Mohit AlRegib, Ghassan OLIVES The Center for Signal and Information Processing CSIP School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States
Self-supervised models create representation spaces that lack clear semantic meaning. This interpretability problem of representations makes traditional explainability methods ineffective in this context. In this pape... 详细信息
来源: 评论
INTELLIGENT MULTI-VIEW TEST TIME AUGMENTATION
arXiv
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arXiv 2024年
作者: Ozturk, Efe Prabhushankar, Mohit AlRegib, Ghassan OLIVES The Center for Signal and Information Processing CSIP School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States
In this study, we introduce an intelligent Test Time Augmentation (TTA) algorithm designed to enhance the robustness and accuracy of image classification models against viewpoint variations. Unlike traditional TTA met... 详细信息
来源: 评论
HEX: Hierarchical Emergence Exploitation in Self-Supervised Algorithms
arXiv
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arXiv 2024年
作者: Kokilepersaud, Kiran Kim, Seulgi Prabhushankar, Mohit AlRegib, Ghassan OLIVES The Center for Signal and Information Processing CSIP School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States
In this paper, we propose an algorithm that can be used on top of a wide variety of self-supervised (SSL) approaches to take advantage of hierarchical structures that emerge during training. SSL approaches typically w... 详细信息
来源: 评论
Multimedia Classification via Tensor Linear Discriminant Analysis
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IEEE Transactions on Broadcasting 2024年 第4期70卷 1139-1152页
作者: Chang, Shih-Yu Wu, Hsiao-Chun Yan, Kun Chih-Hao Huang, Scott Wu, Yiyan San Jose State University Department Of Applied Data Science San JoseCA95192 United States Louisiana State University School Of Electrical Engineering And Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center For Ai Applications Taoyuan32003 Taiwan Guilin University Of Electronic Technology Guangxi Key Laboratory Of Wireless Wideband Communication And Signal Processing Department Of Information And Telecommunication Guilin541004 China National Tsing Hua University Department Of Electrical Engineering Hsinchu300 Taiwan Western University Department Of Electrical And Computer Engineering LondonONN6A 3K7 Canada
Linear discriminant analysis (LDA) is a well-known feature-extraction technique for data analytic and pattern classification. As the dimensionality of multimedia data has increased in this big era, it is often to char... 详细信息
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Complementary Phase Encoding for Pair-Wise Neural Deblurring of Accelerated Brain MRI  17th
Complementary Phase Encoding for Pair-Wise Neural Deblurrin...
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17th European Conference on computer Vision, ECCV 2022
作者: Hod, Gali Green, Michael Waserman, Mark Konen, Eli Shrot, Shai Nelkenbaum, Ilya Kiryati, Nahum Mayer, Arnaldo School of Electrical Engineering Tel Aviv University Tel Aviv Israel School of Computer Sciences Ben Gurion University Beersheba Israel Diagnostic Imaging Department at Sheba Medical Center Affiliated with the Beverly Sackler School of Medicine Tel Aviv University Tel Aviv Israel Klachky Chair of Image Processing School of Electrical Engineering Tel Aviv University Tel Aviv Israel
MRI has become an invaluable tool for diagnostic brain imaging, providing unrivalled qualitative and quantitative information to the radiologist. However, due to long scanning times and capital costs, access to MRI la... 详细信息
来源: 评论
Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm
IEEE Transactions on Artificial Intelligence
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IEEE Transactions on Artificial Intelligence 2023年 第2期4卷 268-281页
作者: Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany
The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision s... 详细信息
来源: 评论