TY - GEN
T1 - Learning invariant feature hierarchies
AU - LeCun, Yann
N1 - Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - Fast visual recognition in the mammalian cortex seems to be a hierarchical process by which the representation of the visual world is transformed in multiple stages from low-level retinotopic features to high-level, global and invariant features, and to object categories. Every single step in this hierarchy seems to be subject to learning. How does the visual cortex learn such hierarchical representations by just looking at the world? How could computers learn such representations from data? Computer vision models that are weakly inspired by the visual cortex will be described. A number of unsupervised learning algorithms to train these models will be presented, which are based on the sparse auto-encoder concept. The effectiveness of these algorithms for learning invariant feature hierarchies will be demonstrated with a number of practical tasks such as scene parsing, pedestrian detection, and object classification.
AB - Fast visual recognition in the mammalian cortex seems to be a hierarchical process by which the representation of the visual world is transformed in multiple stages from low-level retinotopic features to high-level, global and invariant features, and to object categories. Every single step in this hierarchy seems to be subject to learning. How does the visual cortex learn such hierarchical representations by just looking at the world? How could computers learn such representations from data? Computer vision models that are weakly inspired by the visual cortex will be described. A number of unsupervised learning algorithms to train these models will be presented, which are based on the sparse auto-encoder concept. The effectiveness of these algorithms for learning invariant feature hierarchies will be demonstrated with a number of practical tasks such as scene parsing, pedestrian detection, and object classification.
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U2 - 10.1007/978-3-642-33863-2_51
DO - 10.1007/978-3-642-33863-2_51
M3 - Conference contribution
AN - SCOPUS:84867711674
SN - 9783642338625
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 496
EP - 505
BT - Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings
PB - Springer Verlag
T2 - Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings
Y2 - 7 October 2012 through 13 October 2012
ER -