@inproceedings{3a6e6cc3ac6e4326b35a6315d7cbddf7,
title = "Object recognition with gradient-based learning",
abstract = "Finding an appropriate set of features s an essential problem in the design of shape recognition systems. This paper attempts to show that for recognizing simple objects with high shape variability such as handwritten characters, it is possible, and even advantageous, to feed the system directly with minimally processed images and to rely on learning to extract the right set of features. Convolutional Neural Networks are shown to be particularly well suited to this task. We also show that these networks can be used to recognize multiple objects without requiring explicit segmentation of the objects from their surrounding. The second part of the paper presents the Graph Transformer Network model which extends the applicability of gradient-based learning to systems that use graphs to represents features, objects, and their combinations.",
author = "Yann LeCun and Patrick Haffner and L{\'e}on Bottou and Yoshua Bengio",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1999.; International Workshop on Shape, Contour and Grouping in Computer Vision ; Conference date: 26-05-1998 Through 29-05-1998",
year = "1999",
doi = "10.1007/3-540-46805-6_19",
language = "English (US)",
isbn = "3540667229",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "319--345",
editor = "Forsyth, {David A.} and Mundy, {Joseph L.} and {di Gesu}, Vito and Roberto Cipolla",
booktitle = "Shape, Contour and Grouping in Computer Vision",
}