Probabilistic Graphical Models - Cvlibs · Probabilistic Graphical Models Andreas Geiger Autonomous...
Transcript of Probabilistic Graphical Models - Cvlibs · Probabilistic Graphical Models Andreas Geiger Autonomous...
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Probabilistic Graphical Models
Andreas Geiger
Autonomous Vision GroupMPI Tubingen
Computer Vision and Geometry LabETH Zurich
January 13, 2017
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Autonomous Vision GroupResearch
Dr. Andreas GeigerGroup Leader
Dr. Osman UlusoyPostdoc
Fatma GüneyPhD Student
Aseem BehlPhD Student
Lars MeschederPhD Student
Joel JanaiPhD Student
Benjamin CoorsPhD Student
Yiyi LiaoVisiting PhD Student
Gernot RieglerVisiting PhD Student
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Autonomous Vision GroupResearch
Computer Vision Robotics
Machine LearningGraphical Models Deep Learning
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Class Requirements & Materials
Requirements:
� Linear Algebra (matrices, vectors)
� Probability Theory (marginals, conditionals)
Materials: www.cvlibs.net/learn
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Machine Learning
“Field of study that gives computers the ability to learn without beingexplicitly programmed.” Arthur Samuel, 1959
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Machine Learning
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Machine Learning
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Machine Learning
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Machine Learning
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Graphical Models
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Supervised Learning
fθInput Output
Model
� Learning: Estimate parameters θ from training data
� Inference: Make novel predictions fθ(·)
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Classification
fθ
Input
"Beach"
Model Output
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Structured Prediction
fθ
Input Model Outputt=1
t=2
t=3t=1 t=2 t=3x1 x2 x3
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Vehicle Localization
lane 1lane 2lane 3
x1=? x2=? x3=? x4=? x5=? x6=? x7=? x8=? x9=? x10=?
� Goal: Estimate vehicle location at time t = 1, . . . , 10
� Variables: x = {x1, . . . , x10} xi ∈ {1, 2, 3}� Observations: y = {y1, . . . , y10} yi ∈ R3
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Markov Random Field
f1 f2 f3g gx1 x2 x3
f10gx10
g
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Markov Random Field
f1 f2 f3g gx1 x2 x3
f10gx10
g
pθ(x|y) =1
Z
10∏i=1
fi (xi )9∏
i=1
gθ(xi , xi+1)
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Markov Random Field
f1 f2 f3g gx1 x2 x3
f10gx10
g
pθ(x|y) =1
Z
10∏i=1
fi (xi )9∏
i=1
gθ(xi , xi+1)
Unary Factors:
� f1(x1) =
0.70.20.1
, f2(x2) =
0.70.10.2
, f3(x3) =
0.20.10.7
, . . .
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Markov Random Field
f1 f2 f3g gx1 x2 x3
f10gx10
g
pθ(x|y) =1
Z
10∏i=1
fi (xi )9∏
i=1
gθ(xi , xi+1)
Pairwise Factors:
� gθ(xi , xi+1) =
θ11 θ12 θ13θ21 θ22 θ23θ31 θ32 θ33
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Markov Random Field
f1 f2 f3g gx1 x2 x3
f10gx10
g
pθ(x|y) =1
Z
10∏i=1
fi (xi )9∏
i=1
gθ(xi , xi+1)
Pairwise Factors:
� gθ(xi , xi+1) =
θ11 θ12 θ13θ21 θ22 θ23θ31 θ32 θ33
� Learning Problem:
θ∗ = argmaxθ
∏n
pθ(xn|yn)
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Markov Random Field
f1 f2 f3g gx1 x2 x3
f10gx10
g
pθ(x|y) =1
Z
10∏i=1
fi (xi )9∏
i=1
gθ(xi , xi+1)
Pairwise Factors:
� gθ(xi , xi+1) =
0.8 0.2 0.00.2 0.6 0.20.0 0.2 0.8
� Change 2 lanes: 0%
� Change 1 lane: 20%
� Otherwise: stay on lane
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Inference
f1 f2 f3g gx1 x2 x3
f10gx10
g
� Maximum-A-Posteriori State:
x1, . . . , x10 = argmaxx1,...,x10
pθ(x1, . . . , x10|y)
� Marginal Distribution:
p(x1) =∑x2
∑x3
· · ·∑x10
pθ(x1, . . . , x10|y)
� Complexity?
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Inference
f1 f2 f3g gx1 x2 x3
f10gx10
g
� Maximum-A-Posteriori State:
x1, . . . , x10 = argmaxx1,...,x10
pθ(x1, . . . , x10|y)
� Marginal Distribution:
p(x1) =∑x2
∑x3
· · ·∑x10
pθ(x1, . . . , x10|y)
� Complexity?
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Inference
f1 f2 f3g gx1 x2 x3
f10gx10
g
� Maximum-A-Posteriori State:
x1, . . . , x10 = argmaxx1,...,x10
pθ(x1, . . . , x10|y)
� Marginal Distribution:
p(x1) =∑x2
∑x3
· · ·∑x10
pθ(x1, . . . , x10|y)
� Complexity?
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Inference
f1 f2 f3g gx1 x2 x3
f10gx10
g
� Maximum-A-Posteriori State:
x1, . . . , x10 = argmaxx1,...,x10
pθ(x1, . . . , x10|y)
� Marginal Distribution:
p(x1) =∑x2
∑x3
· · ·∑x10
pθ(x1, . . . , x10|y)
� Complexity? O(#states#nodes) Here: 310 = 59049
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Inference
f1 f2 f3g gx1 x2 x3
p(x1, x2, x3) =1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
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Inference
f1 f2 f3g gx1 x2 x3
p(x1, x2, x3) =1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
p(x1) =∑x2,x3
1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
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Inference
f1 f2 f3g gx1 x2 x3
p(x1, x2, x3) =1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
p(x1) =∑x2,x3
1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
=1
Zf1(x1)
∑x2
f2(x2) g(x1, x2)∑x3
f3(x3) g(x2, x3)
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Inference
f1 f2 f3g gx1 x2 x3
μ(x2)
p(x1, x2, x3) =1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
p(x1) =∑x2,x3
1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
=1
Zf1(x1)
∑x2
f2(x2) g(x1, x2)∑x3
f3(x3) g(x2, x3)︸ ︷︷ ︸µ(x2)
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Inference
f1 f2 f3g gx1 x2 x3
μ(x2)
p(x1, x2, x3) =1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
p(x1) =∑x2,x3
1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
=1
Zf1(x1)
∑x2
f2(x2) g(x1, x2)µ(x2)
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Inference
f1 f2 f3g gx1 x2 x3
μ'(x1)
p(x1, x2, x3) =1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
p(x1) =∑x2,x3
1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
=1
Zf1(x1)
∑x2
f2(x2) g(x1, x2)µ(x2)︸ ︷︷ ︸µ′(x1)
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Inference
f1 f2 f3g gx1 x2 x3
μ'(x1)
p(x1, x2, x3) =1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
p(x1) =∑x2,x3
1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
=1
Zf1(x1)µ
′(x1)
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Inference
f1 f2 f3g gx1 x2 x3
μ'(x1)
p(x1, x2, x3) =1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
p(x1) =∑x2,x3
1
Zf1(x1) f2(x2) f3(x3) g(x1, x2) g(x2, x3)
=1
Zf1(x1)µ
′(x1)
Marginal = Product of Factor & Incoming Messages
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J. Pearl: Reverend Bayes on inference engines:A distributed hierarchical approach. AAAI, 1982.
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Inference
Observations
lane 1lane 2lane 3
Try yourself: www.cvlibs.net/learn
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Inference
Marginal Distributions
lane 1lane 2lane 3
Try yourself: www.cvlibs.net/learn
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Inference
Maximum-A-Posteriori State
lane 1lane 2lane 3
Try yourself: www.cvlibs.net/learn
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