The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Unsupervised learning of hierarchical representations with convolutional deep belief networks
Open accessAbstract
There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks (DBNs); however, scaling such models to full-sized, high-dimensional images remains a difficult problem. To address this problem, we present the convolutional deep belief network , a hierarchical generative model that scales to realistic image sizes. This model is translation-invariant and supports efficient bottom-up and top-down probabilistic inference. Key to our approach is probabilistic max-pooling , a novel technique that shrinks the representations of higher layers in a probabilistically sound way. Our experiments show that the algorithm learns useful high-level visual features, such as object parts, from unlabeled images of objects and natural scenes. We demonstrate excellent performance on several visual recognition tasks and show that our model can perform hierarchical (bottom-up and top-down) inference over full-sized images.
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Cites
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Deep learning for computer vision: A brief review
Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/7068349 · Cited by 3,403
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Metadata
- Authors
- Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y.
- Venue
- Communications of the ACM
- Published
- 2011-10
- Volume
- 54
- Issue
- 10
- Pages
- 95-103
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W1971014294
- Cited by
- 388
- References
- 37
- FWCI
- 19.67
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2011). Unsupervised learning of hierarchical representations with convolutional deep belief networks. Communications of the ACM. https://doi.org/10.1145/2001269.2001295
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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