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Lie group impression for deep learning
In this work, we exploit a novel algorithm for capturing the Lie group manifold structure of the visual impression. By developing the single-layer Lie group model, we show how the representation learning algorithm can be stacked to yield a deep architecture. In addition, we design a Lie group based gradient descent algorithm to solve the learning problem of network weights. We show that our proposed technique yields representations that significantly better suited for training deep network and is also computationally efficient.
Lie group impression for deep learning
In this work, we exploit a novel algorithm for capturing the Lie group manifold structure of the visual impression. By developing the single-layer Lie group model, we show how the representation learning algorithm can be stacked to yield a deep architecture. In addition, we design a Lie group based gradient descent algorithm to solve the learning problem of network weights. We show that our proposed technique yields representations that significantly better suited for training deep network and is also computationally efficient.
Lie group impression for deep learning
Yang, Mengduo (author) / Li, Fanzhang (author)
2017-09-01
220507 byte
Conference paper
Electronic Resource
English
Deep learning algorithm with visual impression
| IEEE | 2017
| Online Contents | 2000
| DataCite | 1917
| British Library Online Contents | 1997