VS298: Natural Scene Statistics

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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


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

Additional reading:

  • Attneave (1954) - 'Some informational aspects of visual perception' paper
  • Laughlin (1981) - Histogram equalization of contrast response paper
  • Srinivasan (1982) - 'Predictive coding: a fresh view of inhibition in the retina' paper
  • Switkes (1978) - Power spectrum of carpentered environments paper
  • Ruderman (1997) - Why are images 1/f2? paper
  • Torralba & Oliva (2003) - Power spectrum of natural image categories 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

Additional reading:

  • Dong & Atick (1995) - spatiotemporal decorrelation using lagged and non-lagged cells paper
  • Doi & Lewicki (2007) - A theory of retinal population coding paper

Chayut Thanapirom
Michael Levy
Yubei Chen

Feb. 17 ** Holiday **
Feb. 24 Higher-order statistics and sensory coding
  • Barlow (1972): Sparse coding paper
  • Field (1994): What is the goal of sensory coding? paper
  • Bell & Sejnowski (1995): Independent component analysis. paper

Additional reading:

  • Redlich (1993): Redundancy Reduction as a Strategy for Unsupervised Learning. paper
  • Baddeley (1996): Searching for filter with 'interesting' output distributions: An uninteresting direction to explore? paper
  • O'regan & Noe (2001): A sensorimotor account of vision and visual consciousness paper

Karl Zipser
Michael Levy
Mayur Mudigonda

March 3 ICA and sparse coding of natural images
  • Bell & Sejnowski (1997): ICA of natural images paper
  • Olshausen & Field (1997): Sparse coding of natural images paper
  • van Hateren & Ruderman (1998), Olshausen (2003): ICA/sparse coding of natural video paper1, paper2

Additional reading:

  • Olshausen & Field (1996): simpler explanation of sparse coding paper

Mayur Mudigonda
Zayd Enam
Georgios Exarchakis

March 11 **Tuesday** Statistics of natural sound and auditory coding
  • Clark & Voss: '1/f noise and music' paper
  • Smith & Lewicki: sparse coding of natural sound paper
  • Klein/Deweese: ICA/sparse coding of spectrograms paper1, paper2

Tyler Lee
Yubei Chen

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

Chayut Thanapirom
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

Evan Shelhamer
Brian Cheung
Chris Warner

April 7 Learning invariances through 'slow feature analysis'
  • Foldiak/Wiskott: slow feature analysis
  • Hyvarinen: 'Bubbles'
  • Berkes et al.: factorizing 'what' and 'where' from video

Guy Isely
Chayut Thanapirom

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

James Arnemann

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): Unsupervised learning (Google brain, 'cat' neurons)
  • Krishevsky et al. (2012): Supervised learning, ImageNet 1000 paper
  • Fergus (2013): visualizing what deep nets learn paper

Reza Abbasi-Asl
Shiry Ginosar

May 5 Special topics
  • Image compression with Hopfield networks

Chris Hillar

May 12 Special topics