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Exploiting Invariance in Training Deep Neural Networks


Mar 30, 2021
Chengxi Ye, Xiong Zhou, Tristan McKinney, Yanfeng Liu, Qinggang Zhou, Fedor Zhdanov


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Network Deconvolution


May 28, 2019
Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Thomas Goldstein, James A. Yorke, Cornelia Fermüller, Yiannis Aloimonos


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EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras


Mar 18, 2019
Anton Mitrokhin, Chengxi Ye, Cornelia Fermuller, Yiannis Aloimonos, Tobi Delbruck

* 8 pages, 6 figures. Submitted to 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019) 

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Unsupervised Learning of Dense Optical Flow, Depth and Egomotion from Sparse Event Data


Feb 25, 2019
Chengxi Ye, Anton Mitrokhin, Cornelia Fermüller, James A. Yorke, Yiannis Aloimonos


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Unsupervised Learning of Dense Optical Flow and Depth from Sparse Event Data


Sep 23, 2018
Chengxi Ye, Anton Mitrokhin, Chethan Parameshwara, Cornelia Fermüller, James A. Yorke, Yiannis Aloimonos


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Evenly Cascaded Convolutional Networks


Jul 27, 2018
Chengxi Ye, Chinmaya Devaraj, Michael Maynord, Cornelia Fermüller, Yiannis Aloimonos


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On the Importance of Consistency in Training Deep Neural Networks


Aug 02, 2017
Chengxi Ye, Yezhou Yang, Cornelia Fermuller, Yiannis Aloimonos


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Spectral Graph Cut from a Filtering Point of View


Nov 08, 2016
Chengxi Ye, Yuxu Lin, Mingli Song, Chun Chen, David W. Jacobs

* This version is uploaded for better readability, and was last modified in Oct, 2012 

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LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning


Aug 02, 2016
Chengxi Ye, Chen Zhao, Yezhou Yang, Cornelia Fermuller, Yiannis Aloimonos

* Accepted to ACM MULTIMEDIA 2016 Open Source Software Competition 

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What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots


Feb 02, 2016
Chengxi Ye, Yezhou Yang, Cornelia Fermuller, Yiannis Aloimonos


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Sparse Norm Filtering


May 17, 2013
Chengxi Ye, Dacheng Tao, Mingli Song, David W. Jacobs, Min Wu


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