Chengxi Ye | DeepAI

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∙ 03/30/2021

Exploiting Invariance in Training Deep Neural Networks

Inspired by two basic mechanisms in animal visual systems, we introduce ...

0 Chengxi Ye, et al. ∙

∙ 05/28/2019

Network Deconvolution

Convolution is a central operation in Convolutional Neural Networks (CNN...

2 Chengxi Ye, et al. ∙

∙ 03/18/2019

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

We present the first event-based learning approach for motion segmentati...

22 Anton Mitrokhin, et al. ∙

∙ 09/23/2018

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

In this work we present unsupervised learning of depth and motion from s...

2 Chengxi Ye, et al. ∙

∙ 07/02/2018

Evenly Cascaded Convolutional Networks

In this paper we demonstrate that state-of-the-art convolutional neural ...

2 Chengxi Ye, et al. ∙

∙ 08/02/2017

On the Importance of Consistency in Training Deep Neural Networks

We explain that the difficulties of training deep neural networks come f...

0 Chengxi Ye, et al. ∙

∙ 05/09/2016

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

LightNet is a lightweight, versatile and purely Matlab-based deep learni...

0 Chengxi Ye, et al. ∙

∙ 01/29/2016

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

For robots that have the capability to interact with the physical enviro...

0 Chengxi Ye, et al. ∙

∙ 05/17/2013

Sparse Norm Filtering

Optimization-based filtering smoothes an image by minimizing a fidelity ...

0 Chengxi Ye, et al. ∙

∙ 05/20/2012

Spectral Graph Cut from a Filtering Point of View

Spectral graph theory is well known and widely used in computer vision. ...

0 Chengxi Ye, et al. ∙

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