IEEE - Institute of Electrical and Electronics Engineers, Inc. - An Empirical Evaluation of Current Convolutional Architectures’ Ability to Manage Nuisance Location and Scale Variability

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Author(s): Nikolaos Karianakis ; Jingming Dong ; Stefano Soatto
Publisher: IEEE - Institute of Electrical and Electronics Engineers, Inc.
Publication Date: 1 June 2016
Conference Location: Las Vegas, NV, USA
Conference Date: 27 June 2016
Page(s): 4,442 - 4,451
ISBN (Electronic): 978-1-4673-8851-1
ISSN (Electronic): 1063-6919
DOI: 10.1109/CVPR.2016.481
Regular:

We conduct an empirical study to test the ability of convolutional neural networks (CNNs) to reduce the effects of nuisance transformations of the input data, such as location, scale and aspect... View More

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