Talk slide at SIGGRAPH 2017. Project page: http://koyama.xyz/project/sequential_line_search/

1. Sequential Line Search for Efficient
Visual Design Optimization by Crowds
Takeo IgarashiIssei SatoYuki Koyama
Daisuke
Sakamoto

2. Motivation

3. Parameter Tweaking Based on Preference

4. Design Exploration Optimization
x⇤
= arg max
x2X
Goodness(x)

5. Optimization
x⇤
= arg max
x2X
Goodness(x)

6. Human-in-the-Loop
Optimization

7. Crowdsourced Human Computation
0110
0101
1101
Alexander J. Quinn and Benjamin B. Bederson. 2011. Human computation: a survey and
taxonomy of a growing field. In Proc. CHI '11. 1403-1412.

8. Related Work on Crowdsourced Human Computation

9. Contributions

10. Contributions

17. Microtask Design

18.
Microtask Design

19. 😁

20. Technical Challenges

21. Technical Background:
(Standard) Bayesian Optimization

22. (Standard) Bayesian Optimization

32. New Technique:
Bayesian Optimization
Based on Line Search

33. Our Method

34. How to Define Slider Spaces
S

35. How to Define Slider Spaces
x+
= arg max
x2{xi}
µ(x)
xEI
= arg max
x2X
EI(x)

51. Web Interface
for Crowdsourcing

53. Applications #1
Photo Color Enhancement (6D)

56. Evaluation: Crowdsourced Voting

60. Applications #2
Material Appearance (3D / 7D)

68. Comparative Evaluation

69. Comparative Evaluation

71. Experiment #1: Synthetic Setting

72. 0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0 5 10 15 20
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0 5 10 15 20
Optimizing a 2D function

73. Optimizing a 6D function
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 5 10 15 20
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0 5 10 15 20

74. Optimizing a 20D function
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
0 5 10 15 20
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
0 5 10 15 20

76. Experiment #2: Crowdsourcing Setting

77. Experiment #2: Crowdsourcing Setting
0
5
10
15
20
25
30
35
0 2 4 6 8 10 12 14 16 18 20
0
5
10
15
20
25
30
35
0 2 4 6 8 10 12 14 16 18 20

78. Summary

79. Summary
Concept:
Strategy:
Technique:
Applications:

80. Limitation & Future Work

81. Sequential Line Search for Efficient
Visual Design Optimization by Crowds
Takeo IgarashiIssei SatoYuki Koyama
Daisuke
Sakamoto

83. Optimization with Different Initial Conditions

85. Task Burden (Completion Time)
0
10
20
30
40
50
60
70
80
90
SSM 2GC 4GC
TaskCompletionTime[s]

86. Advantages of Involving Many Crowds

87. Assumptions on Design Domains

88. Assumptions on Crowds

89. Difficult Cases
SIGGRAPH
SIGGRAPH
SIGGRAPH