발표자 : 백인성

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1. Introduction

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이번 행사의성공 가능성을분석해 보게

네. 지난 행사기록 중심으로분석해 보겠습

니다.

[사업부 팀장] [1년차 신입사원]

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분석 결과,이번행사 성공률은80%입니다.

근데 왜?? 무엇 때문인지알아야 우리가기획을 하지

[사업부 팀장][1년차 신입사원]

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현업에 있는분들 설득하려면근거가 필요해요.

모델에서 중요하다고 하는 변수와저희 예상과 비슷한 지 비교해보죠

[B사 팀장][A사 팀장]

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1. 예측 모델을 실제로 사용하는 사람들의 이해를 돕기 위해

2. 의사결정하는 사람들을 조금 더 쉽게 설득하기 위해

3. 구축한 예측 모델의 결과의 타당함을 직관적으로 보이기 위해

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2. [CNN]Convolutional Neural Network

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[Input data][Convolutional Layer] [Neural Network]

아이언맨 : 80%

워머신 : 10%

스파이더맨: 8%

캡틴아메리카: 2%

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3. [CAM]Class Activation Map

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3 2 0

5 2 1

5 0 9

GAP

[3x3x3(Channel)]

3

[1x1x3(channel)]

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1 0 9

3 2 7

4 3 2

0 0 7

5 2 1

2 2 1

3 3 0

5 2 1

5 0 9

* 𝑤1

* 𝑤2

* 𝑤3

4. Interpretable Convolutional Neural Network

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2 1 0 1

3 7 19 11

0 5 14 5

0 1 2 1

1 0

0 5

[After ReLu Activation] [Filter]

37 96 55

28 77 46

5 15 19

[Feature map]

0 2

1 1

12 26 32

19 57 41

11 31 12

Step1.

Step2.

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𝜇𝑖로 지정한 Template을 구성

1 0

0 5

[Filter]

37 96 55

28 77 46

5 15 19

[Feature map_Train]

0 2

1 1

12 26 32

19 57 41

11 31 12

Step1.

Step2.

[Template]

𝜇𝑖

𝑥𝑖

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9 21 30

5 100 51

0 21 22

[Feature map_Test]

1 11 7

21 88 62

9 30 51

[Template_cat] [after_Template]

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Data 내용 이미지

Animal-PartHeads and legs of

30 animals

CUB200-2011Bird image of 200

pieces

Pascal VOC Part107 object

landmarks in six animal categories

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𝑑1 𝑝𝑘, Ƹ𝜇 =𝑝𝑘 − 𝑝( Ƹ𝜇)

𝑤2 + ℎ2

Normalized distance :

𝐷𝑓,𝑘 = 𝑣𝑎𝑟𝐼 𝑑𝐼(𝑝𝑘,ෝ𝜇)

Relative location deviation :

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𝑑1 𝑝𝑘, Ƹ𝜇 =𝑝𝑘 − 𝑝( Ƹ𝜇)

𝑤2 + ℎ2

Normalized distance :

𝐷𝑓,𝑘 = 𝑣𝑎𝑟𝐼 𝑑𝐼(𝑝𝑘,ෝ𝜇)

Relative location deviation :

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𝑑1 𝑝𝑘, Ƹ𝜇 =𝑝𝑘 − 𝑝( Ƹ𝜇)

𝑤2 + ℎ2

Normalized distance :

𝐷𝑓,𝑘 = 𝑣𝑎𝑟𝐼 𝑑𝐼(𝑝𝑘,ෝ𝜇)

Relative location deviation :

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𝑑1 𝑝𝑘, Ƹ𝜇 =𝑝𝑘 − 𝑝( Ƹ𝜇)

𝑤2 + ℎ2

Normalized distance :

𝐷𝑓,𝑘 = 𝑣𝑎𝑟𝐼 𝑑𝐼(𝑝𝑘,ෝ𝜇)

Relative location deviation :

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𝑑1 𝑝𝑘, Ƹ𝜇 =𝑝𝑘 − 𝑝( Ƹ𝜇)

𝑤2 + ℎ2

Normalized distance :

𝐷𝑓,𝑘 = 𝑣𝑎𝑟𝐼 𝑑𝐼(𝑝𝑘,ෝ𝜇)

Relative location deviation :

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5. Conclusion

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감사합니다.

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[CAM Last Layer] [Interpretable CNN Last Layer]

Figure: Basic architecture of Convolutional Neural Networks(https://pydeeplearning.weebly.com/blog/basics-

of-convolutional-neural-networks)

https://bcho.tistory.com/1149(조대협의 블로그)

https://blog.ees.guru/50 (EeS의 연구실)

Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu(2018). Interpretable Convolutional Neural Networks.

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 8827-8836)

http://taewan.kim/post/cnn/

https://medium.com/@kamwohng/interpretable-convolutional-neural-network-3f7ef6c9b7ae