AS.050.375, AS.050.675

Probabilistic Models of the Visual Cortex:
Tues/Thurs: 9:00-10:15am, Krieger 205, Fall 2021.

Course Description

The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning.

Important information

Reading and Background Material


Grading Plan: 5 homework assignments will be posted here(roughly biweekly). The assignments need to be submitted via Gradescope (Entry Code: N8JYDJ).

Homework 1 Due on Oct 5 before class.
Homework 2 Due on Oct 26 before class.
Homework 3 Due on Nov 16 before class.
Homework 4 Due on Dec 14 11:59pm.
Homework 5 Optional to submit.

Preliminary Schedule (based on the last year's schedule and will be updated as the semester goes)

Lecture Date Topics


Required Reading Optional Reading


Introduction  Slides YuilleKersten (Section 1.1, 1.2) An extended version of the lecture:Slides


Introduction to Retina and Primary Visual Cortex (V1) Retina V1 YuilleKersten (Section 1.3)  ReadMe Lecture by Clay Reid V1_Mike_May BialekPhotoReceptors GollischRetina VisualCrowding
3 Sept-7 Linear Filtering Slides YuilleKersten (Section 2.1) KokkinosLinearFiltering CarandiniEarlyVisualSystem


Sparsity Sparsity YuilleKersten (Section 2.2) SparsityPowerpoints SparsityYuilleKersten Mini-Epitomes Background redeading:BarlowSparsity1972
5 Sept-14 Filters for Binocular Stereo and Motion Stereo Motion Figures YuilleKersten (Section 2.4)  


Hebbian Learning and Regression Hebbian Regression YuilleKersten (Section 2.3) TalebiV1RF GallantNaturalStimulus ZhangCNN ZhangCNNV1Patterns


Vision as Bayesian Inference: Edges VisionAsBayesianInference VisionAsProbabilisticInference YuilleKersten (Section 3.1, 3.2) chater2006probabilistic yuille2006vision
8 Sept-23 Bayes Decision Theory BayesDecisionTheoryYuilleKersten YuilleKersten (Section 3.1, 3.2) YuilleLecture2UCLA
9 Sept-28 Cue Coupling Weak Slides Yuille&Buelthoff(1993)
10 Sept-30 Cue Coupling II CueCouplingStrong DivisiveNormalization   ProbModelsOnGraphs
11 Oct-5 Context and Markov Random Fields Slides YuilleKersten (Section 4) BeliefPropagationMFT
12 Oct-7 Context Examples Slides YuilleKersten (Section 4) Same as Lecture 11
13 Oct-12 Motion and Kalman Filter Slides BarlowTripathy Burgi et al. A198
14 Oct-14 Boltzmann Machines & More Context Examples Slides   TS Lee (2014)
15 Oct-19 Intro to Deep Nets Slides FerusVittal MathDetails HintonAlexNet   YaminsNature2016 Yuille2020
16 Oct-21 Adversarial Machine Learning Jason_Yosinski Slides PatchAttack ZhouFirestone  
17 Oct-26 Interpretable Deep Networks UnsupervisedNAS QiaoFewShot   Bolei Zhou UnsupervisedDeepNets SmirnakisYuille1994 UnsupervisedFlow
18 Oct-28 The 3D world and unsupervised learning Lambertian model of reflectance1 Lambertian model of reflectance2   EveryPixelCountsChenxuLuo2018 LambertianLighting
19 Nov-2 The 3D world and unsupervised learning(continue) GeometryAndMotion Motion_Geometry2020  
20 Nov-4 Human/Animal Parsing BootstrappingDeepNetCueCoupling HumanAnimalParsing ParsingHumansCourse    
21 Nov-9 Learning by Immagination YouOnlyAnnotateOnce PhysicalSceneUnderstanding SimulationEngine  
22 Nov-11 Compositional Models CompositionalTheory ComplexityFundamentalProblem CompositionalModelsLearning   Generative Vision Model
23 Nov-16 Compositional Networks AdversarialPatches CompositionalModelsOcclusion CompNetsAdamK   GeorgeCAPCHAS Detection_with_CompositionalNets Occluder_Localization_with_CompNets HongruZhuCogSci2019
 24 Nov-18 Analysis by Synthesis
AnalysisBySynthesisIntro AnalysisBySynthesisDDMCMC   Image Parsing
  Nov-23 Thanksgiving    
  Nov-25 Thanksgiving  
25 Nov-30 Vision, Language, and Turing Tests JunhuaMaoTextCaptioning CLEVR-Ref+    
26 Dec-2 Model Robustness and Generalizability AdversarialExaminerIntro CompositionalOpenSetActivity