2010
NeurIPS
NeurIPS 2010
Deep Coding Network
Abstract
This paper proposes a principled extension of the traditional single-layer flat sparse coding scheme, where a two-layer coding scheme is derived based on theoretical analysis of nonlinear functional approximation that extends recent results for local coordinate coding. The two-layer approach can be easily generalized to deeper structures in a hierarchical multiple-layer manner. Empirically, it is shown that the deep coding approach yields improved performance in benchmark datasets.
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Trend Setter
- Sparse Coding
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Hot Topic Early Bird
- hierarchical representation
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Core Methods > Embedding Learning
Deep Learning > Architectures > Neural Networks
Machine Learning > Core Methods > Feature Learning
Deep Learning > Learning Types > Representation Learning
Machine Learning > Core Methods > Sparse Coding
Deep Learning > Learning Types > Feature Learning