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Deconvolutional networks
Abstract
Building robust low and mid-level image representations, beyond edge primitives, is a long-standing goal in vision. Many existing feature detectors spatially pool edge information which destroys cues such as edge intersections, parallelism and symmetry. We present a learning framework where features that capture these mid-level cues spontaneously emerge from image data. Our approach is based on the convolutional decomposition of images under a spar-sity constraint and is totally unsupervised. By building a hierarchy of such decompositions we can learn rich feature sets that are a robust image representation for both the analysis and synthesis of images.
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Metadata
- Authors
- Zeiler, M. D., Krishnan, D., Taylor, G. W., & Fergus, R.
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 2528-2535
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2293078015
- Cited by
- 1,666
- References
- 8
- FWCI
- 13.74
- Type
- conference paper
- Language
- en
- Open access
- closed
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