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2017

Submanifold Sparse Convolutional Networks

Graham, Benjamin, van der Maaten, Laurens

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Understand

Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc.

Whilst some of this data is naturally dense (for instance, photos), many other data sources are inherently sparse.
Examples include pen-strokes forming on a piece of paper, or (colored) 3D point clouds that were obtained using a LiDAR scanner or RGB-D camera.
Standard "dense" implementations of convolutional networks are very inefficient when applied on such sparse data.

Submanifold Sparse Convolutional Networks · Around

Built on

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CASIA online and offline chinese handwriting databases
C.-L. Liu, F. Yin, D.-H. Wang, and Q.-F. Wang · 2011
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Going deeper with convolutions
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C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
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