VS298: Natural Scene Statistics

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 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 TBD
March 17	Higher-order group structure Geisler: contour statistics paper Hyvarinen: subspace ICA/topgraphic ICA paper1, paper2 Lyu & Simoncelli: radial Gaussianization paper Additional reading: Parent & Zucker (1989): Trace Inference, Curvature Consistency, and Curve Detection, paper Field et al. (1993): Contour Integration by the Human Visual System: Evidence for a Local “Association Field” paper Zetzsche et al. (1999): The atoms of vision: Cartesian or polar? paper Garrigues & Olshausen (2010): Group Sparse Coding with a Laplacian Scale Mixture Prior, paper	Chayut Thanapirom Guy Isely TBD
March 24	** Spring recess **
March 31	Energy-based models Hinton: Product of experts models, paper Osindero & Hinton: Product of Experts model of natural images, paper Roth & Black: Fields of experts, paper Additional reading: Hinton: Practical guide to training RBMs paper Teh et al: Energy-based models for sparse overcomplete representation, paper	Evan Shelhamer Brian Cheung Chris Warner
April 7	Learning invariances through 'slow feature analysis' Foldiak/Wiskott: slow feature analysis, paper1, paper2 Hyvarinen: 'Bubbles' paper Berkes et al.: factorizing 'what' and 'where' from video, paper	Guy Isely Chayut Thanapirom Bharath Hariharan
April 14	Manifold and Lie group models Carlsson et al.: Klein bottle model of natural images, paper Culpepper & Olshausen: Learning manifold transport operators, paper Roweis & Saul: Local Linear Embedding, paper	Yubei Chen Bruno/Mayur James Arnemann
April 21	Hierarchical models Karklin & Lewicki (2003): density components, paper Shan & Cottrell: stacked ICA, paper Cadieu & Olshausen (2012): learning intermediate representations of form and motion, paper	Tyler Lee Brian Cheung Dylan Paiton
April 28	Deep network models Hinton & Salakhudinov (2006): stacked RBMs, paper Le et al. (2011): Unsupervised learning (Google brain, 'cat' neurons), paper Krishevsky et al. (2012): Supervised learning, ImageNet 1000 paper	TBD TBD Reza Abbasi-Asl
May 6 Note: Tuesday	Special topics Fergus (2013): visualizing what deep nets learn paper Schmidhuber: deep nets (paper), focusing on LOCOCODE (paper) Image compression with Hopfield networks	Shiry Ginosar Anthony DiFranco Chris Hillar
May 12	Special topics