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Difference between revisions of "VS298: Natural Scene Statistics"

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Revision as of 17:23, 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
Feb. 3 Redundancy reduction, whitening, and power spectrum of natural images
  • Barlow (1961): Theory of redundancy reduction paper
  • Atick (1992): Theory of whitening paper
  • Field (1987): 1/f2 power spectrum and sparse coding paper

Anthony DiFranco
Dylan Paiton
Michael Levy

Feb. 10 Whitening in time and color; Robust coding
  • Dong & Atick (1995): spatiotemporal power spectrum of natural movies paper
  • Ruderman (1998): statistics of cone responses paper
  • Karklin & Simoncelli (2012): noisy population coding of natural images paper

Chayut Thanapirom
Michael Levy
Yubei Chen

Feb. 17 ** Holiday **
Feb. 24 Higher-order statistics and sensory coding
  • Barlow (1972): Sparse coding
  • Field (1994): What is the goal of sensory coding?
  • Bell & Sejnowski (1996): Independent components analysis.
March 3 ICA and sparse coding
  • Bell & Sejnowski (1997): ICA of natural images
  • Olshausen & Field (1997): Sparse coding of natural images
  • van Hateren & Ruderman (1998), Olshausen (2003): ICA/sparse coding of natural video
March 10 Statistics of natural sound and auditory coding
  • Clark & Voss: '1/f noise and music'
  • Smith & Lewicki: sparse coding of natural sound
  • Klein/Deweese: ICA/sparse coding of spectrograms

Tyler Lee

March 17 Higher-order group structure
  • Geisler: contour statistics
  • Hyvarinen: subspace ICA/topgraphic ICA
  • Lyu & Simoncelli: radial Gaussianization

Guy Isely

March 24 ** Spring recess **
March 31 Energy-based models
  • Hinton: Restricted Boltzmann machine
  • Osindero & Hinton: Product of Experts
  • Roth & Black: Fields of experts
April 7 Learning invariances through 'slow feature analysis'
  • Foldiak/Wiskott: slow feature analysis
  • Hyvarinen: 'Bubbles'
  • Berkes et al.: factorizing 'what' and 'where' from video
April 14 Manifold and Lie group models
  • Carlsson: Klein bottle model of natural images
  • Culpepper & Olshausen: Learning manifold transport operators
  • Roweis & Saul: Local Linear Embedding
April 21 Hierarchical models
  • Karklin & Lewicki (2003): density components
  • Shan & Cottrell: stacked ICA
  • Cadieu & Olshausen (2012): learning intermediate representations of form and motion
April 28 Deep network models
  • Hinton & Salakhudinov (2006): stacked RBMs
  • Le et al. (2011): Google brain
  • Krishevsky et al. (2012)/Fergus (2013): visualizing deep nets
May 5 Special topics
May 12 Special topics
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