Multistage committees of deep feedforward convolutional sparse denoise autoencoder for object recognition

Deep learning and unsupervised feature learning systems are known to achieve good performance in benchmarks by using extremely large architectures with many features at each layer. However, we found that the number of features' contribution to performance is very small when it is more than the...

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Veröffentlicht in:	2015 Chinese Automation Congress (CAC) S. 565 - 570
Hauptverfasser:	Shicao Luo, Yongsheng Ding, Kuangrong Hao
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.11.2015
Schlagworte:	Convolution deep learning Encoding Feature extraction Kernel multistage classifiers Noise reduction object recognition Robustness sparse denoise autoencoder Training
Online-Zugang:	Volltext
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