TY - JOUR
T1 - Learning Deep and Wide: A Spectral Method for Learning Deep Networks
AU - Shao, Ling
AU - Wu, Di
AU - Li, Xuelong
PY - 2014/12
Y1 - 2014/12
N2 - Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many computer vision-related tasks. We propose the multispectral neural networks (MSNN) to learn features from multicolumn deep neural networks and embed the penultimate hierarchical discriminative manifolds into a compact representation. The low-dimensional embedding explores the complementary property of different views wherein the distribution of each view is sufficiently smooth and hence achieves robustness, given few labeled training data. Our experiments show that spectrally embedding several deep neural networks can explore the optimum output from the multicolumn networks and consistently decrease the error rate compared with a single deep network.
AB - Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many computer vision-related tasks. We propose the multispectral neural networks (MSNN) to learn features from multicolumn deep neural networks and embed the penultimate hierarchical discriminative manifolds into a compact representation. The low-dimensional embedding explores the complementary property of different views wherein the distribution of each view is sufficiently smooth and hence achieves robustness, given few labeled training data. Our experiments show that spectrally embedding several deep neural networks can explore the optimum output from the multicolumn networks and consistently decrease the error rate compared with a single deep network.
KW - Deep networks
KW - multispectral embedding
KW - representation learning
UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6763063
U2 - 10.1109/TNNLS.2014.2308519
DO - 10.1109/TNNLS.2014.2308519
M3 - Article
VL - 25
SP - 2303
EP - 2308
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
SN - 2162-237X
IS - 12
ER -