VS298: Unsolved Problems in Vision

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This seminar is about unsolved problems in vision.

blah blah

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 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

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
  • Zhu, Wu & Mumford: FRAME (Filters, random fields, and maximum entropy), 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
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

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
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