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2017

Visualizing Residual Networks

Chu, Brian, Yang, Daylen, Tadinada, Ravi

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Residual networks are the current state of the art on ImageNet.

Similar work in the direction of utilizing shortcut connections has been done extremely recently with derivatives of residual networks and with highway networks.
This work potentially challenges our understanding that CNNs learn layers of local features that are followed by increasingly global features.
Through qualitative visualization and empirical analysis, we explore the purpose that residual skip connections serve.

Visualizing Residual Networks · Around

Built on

Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Original
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
Earlier work this paper cites.

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