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检索条件"主题词=Variational autoencoder"
1534 条 记 录,以下是1211-1220 订阅
排序:
variational Deep Representation Learning for Cross-Modal Retrieval  4th
Variational Deep Representation Learning for Cross-Modal Ret...
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4th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)
作者: Yang, Chen Deng, Zongyong Li, Tianyu Liu, Hao Liu, Libo Ningxia Univ Sch Informat Engn Yinchuan 750021 Ningxia Peoples R China Ningxia Municipal & Minist Educ Collaborat Innovat Ctr Ningxia Big Data & Artific Yinchuan 750021 Ningxia Peoples R China
In this paper, we propose a variational deep representation learning (VDRL) approach for cross-modal retrieval. Numerous existing methods map the image and text to the point representations, which is challenging to mo... 详细信息
来源: 评论
Self-attention Based Text Matching Model with Generative Pre-training  19
Self-attention Based Text Matching Model with Generative Pre...
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6th IEEE Cyber Science and Technology Congress (CyberSciTech)
作者: Zhang, Xiaolin Lei, Fengpei Yu, Shengji Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China
Text matching is an important method to judge the semantic similarity of different sentences. Improving the efficiency and accuracy of text matching is the most focus in the field of information matching. In recent ye... 详细信息
来源: 评论
3D Nucleus Instance Segmentation for Whole-Brain Microscopy Images  27th
3D Nucleus Instance Segmentation for Whole-Brain Microscopy ...
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27th International Conference on Information Processing in Medical Imaging (IPMI)
作者: Ma, Junbo Krupa, Oleh Glass, Madison Rose McCormick, Carolyn M. Borland, David Kim, Minjeong Stein, Jason L. Wu, Guorong Univ North Carolina Chapel Hill Dept Psychiat Chapel Hill NC 27514 USA Univ North Carolina Chapel Hill UNC Neurosci Ctr Chapel Hill NC 27599 USA Univ North Carolina Chapel Hill Dept Genet Chapel Hill NC 27599 USA Univ North Carolina Chapel Hill RENCI Chapel Hill NC 27599 USA Univ North Carolina Greensboro Dept Comp Sci Greensboro NC 27412 USA Univ North Carolina Chapel Hill Dept Comp Sci Chapel Hill NC 27514 USA
Tissue clearing and light-sheet microscopy technologies offer new opportunities to quantify the three-dimensional (3D) neural structure at a cellular or even sub-cellular resolution. Although many efforts have been ma... 详细信息
来源: 评论
Towards speech enhancement using a variational U-Net architecture  29
Towards speech enhancement using a variational U-Net archite...
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29th European Signal Processing Conference (EUSIPCO)
作者: Nustede, Eike J. Anemueller, Joern Carl von Ossietzky Univ Oldenburg Computat Audit Grp Dept Med Phys & Acoust Oldenburg Germany Carl von Ossietzky Univ Oldenburg Computat Audit Grp Cluster Excellence Hearing4all Oldenburg Germany
We investigate the viability of a variational U-Net architecture for denoising of single-channel audio data. Deep network speech enhancement systems commonly aim to estimate filter masks, or opt to work on the wavefor... 详细信息
来源: 评论
Towards Source-Aligned variational Models for Cross-Domain Recommendation  21
Towards Source-Aligned Variational Models for Cross-Domain R...
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15th ACM Conference on Recommender Systems (RECSYS)
作者: Salah, Aghiles Tran, Thanh Binh Lauw, Hady W. Rakuten Inst Technol Paris France Singapore Management Univ Singapore Singapore
Data sparsity is a long-standing challenge in recommender systems. Among existing approaches to alleviate this problem, cross-domain recommendation consists in leveraging knowledge from a source domain or category (e.... 详细信息
来源: 评论
IDA-GAN: A Novel Imbalanced Data Augmentation GAN  25
IDA-GAN: A Novel Imbalanced Data Augmentation GAN
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25th International Conference on Pattern Recognition (ICPR)
作者: Yang, Hao Zhou, Yun Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha Hunan Peoples R China
Class imbalance is a widely existed and challenging problem in real-world applications such as disease diagnosis, fraud detection, network intrusion detection and so on. Due to the scarce of data, it could significant... 详细信息
来源: 评论
Controllable Gradient Item Retrieval  21
Controllable Gradient Item Retrieval
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30th World Wide Web Conference (WWW)
作者: Wang, Haonan Zhou, Chang Yang, Carl Yang, Hongxia He, Jingrui Univ Illinois Urbana IL USA Alibaba Grp Hangzhou Peoples R China Emory Univ Atlanta GA 30322 USA
In this paper, we identify and study an important problem of gradient item retrieval. We define the problem as retrieving a sequence of items with a gradual change on a certain attribute, given a reference item and a ... 详细信息
来源: 评论
Open-Set Audio Classification with Limited Training Resources based on Augmentation Enhanced variational Auto-Encoder GAN with Detection-Classification Joint Training  22
Open-Set Audio Classification with Limited Training Resource...
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Interspeech Conference
作者: Kah Kuan Teh Huy Dat Tran ASTAR Inst Infocomm Res Aural & Language Intelligence Dept Singapore Singapore
In this paper, we propose a novel method to address practical problems when deploying audio classification systems in operations that are the presence of unseen sound classes (open-set) and the limitation of training ... 详细信息
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Learning Disentangled Representations with the Wasserstein autoencoder  1
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21st Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
作者: Gaujac, Benoit Feige, Ilya Barber, David UCL London England
Disentangled representation learning has undoubtedly benefited from objective function surgery. However, a delicate balancing act of tuning is still required in order to trade off reconstruction fidelity versus disent... 详细信息
来源: 评论
Classification and Authentication of Mineral Water Samples using Electronic Tongue and Deep Neural Networks  3
Classification and Authentication of Mineral Water Samples u...
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3rd IEEE International Conference on Cognitive Machine Intelligence (IEEE CogMI)
作者: Damarla, Seshu Kumar Zhu, Xiuli Kundu, Madhusree Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada Donghua Univ Dept Informat Sci & Technol Shanghai Peoples R China Natl Inst Technol Rourkela Dept Chem Engn Rourkela Odisha India
Supervised multiclass classifiers based on deep neural networks (one-dimensional convolutional neural network (1D-CNN) and long short-term network (LSTM)) are developed to classify and authenticate mineral water sampl... 详细信息
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