AS.050.375, AS.050.675

Probabilistic Models of the Visual Cortex:
Tues/Thurs: 9:00-10:15am Fall 2020.

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

Homeworks (Submit to Gradescope. Please check the Blackboard for the entry code.)

Homework 1 due on Sep 24 before class.
Homework 2 due on Oct 20 before class.
Homework 3 due on Nov 10 before class.
Homework 4 due on Dec 3 before class.
Homework 5 due on Dec 20 11:59 pm.

Preliminary Schedule (based on the last year's schedule and will be updated in red 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-8 Linear Filtering Slides YuilleKersten (Section 2.1) KokkinosLinearFiltering CarandiniEarlyVisualSystem


Sparsity Sparsity YuilleKersten (Section 2.2) SparsityPowerpoints SparsityYuilleKersten Mini-Epitomes Background redeading:BarlowSparsity1972
5 Sept-15 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-24 Bayes Decision Theory BayesDecisionTheoryYuilleKersten YuilleKersten (Section 3.1, 3.2) YuilleLecture2UCLA
9 Sept-29 Cue Coupling Weak Slides Yuille&Buelthoff(1993)
10 Oct-1 Cue Coupling II CueCouplingStrong DivisiveNormalization   ProbModelsOnGraphs
11 Oct-6 Context and Markov Random Fields Slides YuilleKersten (Section 4) BeliefPropagationMFT
12 Oct-8 Context Examples Slides YuilleKersten (Section 4) Same as Lecture 11
13 Oct-13 Boltzmann Machines & More Context Examples Slides   TS Lee (2014)
14 Oct-15 Motion and Kalman Filter Slides BarlowTripathy Burgi et al. A198
15 Oct-20 Intro to Deep Nets Slides FerusVittal MathDetails HintonAlexNet   YaminsNature2016 Yuille2020
Oct-22 Fall Break  
16 Oct-27 Adversarial Machine Learning Slides PatchAttack Jason_Yosinski ZhouFirestone  
17 Oct-29 Interpretable Deep Networks Jason_Yosinski Bolei Zhou UnsupervisedDeepNets   SmirnakisYuille1994 QiaoFewshot UnsupervisedFlow UnsupervisedNAS
18 Nov-3 The 3D world and unsupervised learning GeometryAndMotion Motion_Geometry2020 LambertianLighting BootstrappingDeepNetCueCoupling   EveryPixelCountsChenxuLuo2018
19 Nov-5 Human/Animal Parsing HumanAnimalParsing ParsingHumansCourse    
20 Nov-10 Learning by Immagination YouOnlyAnnotateOnce PhysicalSceneUnderstanding SimulationEngine  
21 Nov-12 Compositional Models CompositionalTheory ComplexityFundamentalProblem CompositionalModelsLearning   Generative Vision Model
22 Nov-17 Compositional Networks AdversarialPatches CompositionalModelsOcclusion CompNetsAdamK   GeorgeCAPCHAS Detection_with_CompositionalNets Occluder_Localization_with_CompNets HongruZhuCogSci2019
 23 Nov-19 Analysis by Synthesis
AnalysisBySynthesisIntro AnalysisBySynthesisDDMCMC   Image Parsing
  Nov-24 Thanksgiving    
  Nov-26 Thanksgiving  
24 Dec-1 Vision, Language, and Turing Tests JunhuaMaoTextCaptioning CLEVR-Ref+    
25 Dec-3 Model Robustness and Generalizability AdversarialExaminerIntro CompositionalOpenSetActivity    
26 Dec-8 Human Parsing from Static Images and Sequences Key-Pose-Motifs