VS298: Unsolved Problems in Vision

One of the goals of vision science is to understand the nature of perception and its neural substrates. There are now many well established techniques and paradigms in both psychophysics and neuroscience to address problems in vision. However, knowing how to frame these questions for investigation is not necessarily obvious. Nervous systems present us with stunning complexity, and the purpose of perception itself is deeply mysterious. The goal of this seminar course is to step back and ask, what are the important problems that remain unsolved in vision research, and how should these be approached empirically? The course will consist of alternating weeks of discussion and guest lectures by vision scientists who will frame their views of the core unsolved problems. Interdisciplinary groups of students will devise a practical research plan to address an unsolved problem of their choice.

Instructors: Stan Klein, Jerry Feldman, Bruno Olshausen, and Karl Zipser GSI: [1]

Enrollment information:

VS 298 (section 2), 2 units
CCN: 66478

Meeting time and place:

Tuesday 6-8, 489 Minor

Email list:

vs298-unsolved-problems@lists.berkeley.edu subscribe

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 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 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