VS298: Natural Scene Statistics: Difference between revisions
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* Ruderman (1998): statistics of cone responses [https://www.dropbox.com/s/qpo6v0viush5k0o/ruderman-color.pdf paper] <br /> | * Ruderman (1998): statistics of cone responses [https://www.dropbox.com/s/qpo6v0viush5k0o/ruderman-color.pdf paper] <br /> | ||
* Karklin & Simoncelli (2012): noisy population coding of natural images [https://www.dropbox.com/s/m4wc2c2v9p0vqyu/karklin-simoncelli12.pdf paper] | * Karklin & Simoncelli (2012): noisy population coding of natural images [https://www.dropbox.com/s/m4wc2c2v9p0vqyu/karklin-simoncelli12.pdf paper] | ||
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Chayut Thanapirom<br /> | |||
Michael Levy<br /> | |||
Yubei Chen | |||
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| Feb. 17 | | Feb. 17 |
Revision as of 17:19, 4 February 2014
This seminar will examine what is known about the statistical structure of natural visual and auditory scenes, and theories of how sensory coding strategies have been adapted to this structure.
Instructor: Bruno Olshausen
Enrollment information:
VS 298 (section 4), 2 units
CCN: 66489
Meeting time and place:
Monday 6-8, Evans 560
Email list:
nss2014@lists.berkeley.edu subscribe
Readings:
Books and review articles:
- Natural Image Statistics by Hyvarinen, Hurri & Hoyer
- Olshausen BA & Lewicki MS (2013) What natural scene statistics can tell us about cortical representation. In: The Cognitive Neurosciences V. paper
- Geisler WS (2008) Visual perception and the statistical properties of natural scenes. Annual Review of Psychology paper
Weekly schedule:
Date | Topic/Reading | Presenter |
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Feb. 3 | Redundancy reduction, whitening, and power spectrum of natural images | Anthony DiFranco |
Feb. 10 | Whitening in time and color; Robust coding |
Chayut Thanapirom |
Feb. 17 | ** Holiday ** | |
Feb. 24 | Higher-order statistics and sensory coding
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March 3 | ICA and sparse coding
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March 10 | Statistics of natural sound and auditory coding
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March 17 | Higher-order group structure
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March 24 | ** Spring recess ** | |
March 31 | Energy-based models
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April 7 | Learning invariances through 'slow feature analysis'
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April 14 | Manifold and Lie group models
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April 21 | Hierarchical models
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April 28 | Deep network models
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May 5 | Special topics |
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May 12 | Special topics |