TY - GEN
T1 - Deep learning with kernel regularization for visual recognition
AU - Yu, Kai
AU - Xu, Wei
AU - Gong, Yihong
PY - 2009
Y1 - 2009
N2 - In this paper we aim to train deep neural networks for rapid visual recognition. The task is highly challenging, largely due to the lack of a meaningful regularizer on the functions realized by the networks. We propose a novel regularization method that takes advantage of kernel methods, where an oracle kernel function represents prior knowledge about the recognition task of interest. We derive an efficient algorithm using stochastic gradient descent, and demonstrate encouraging results on a wide range of recognition tasks, in terms of both accuracy and speed.
AB - In this paper we aim to train deep neural networks for rapid visual recognition. The task is highly challenging, largely due to the lack of a meaningful regularizer on the functions realized by the networks. We propose a novel regularization method that takes advantage of kernel methods, where an oracle kernel function represents prior knowledge about the recognition task of interest. We derive an efficient algorithm using stochastic gradient descent, and demonstrate encouraging results on a wide range of recognition tasks, in terms of both accuracy and speed.
UR - https://www.scopus.com/pages/publications/84863393567
M3 - 会议稿件
AN - SCOPUS:84863393567
SN - 9781605609492
T3 - Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
SP - 1889
EP - 1896
BT - Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
PB - Neural Information Processing Systems
T2 - 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
Y2 - 8 December 2008 through 11 December 2008
ER -