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A Simple AI Problem
That Wasn’t
Wei-Chao Chen 陳維超
Chief Digital Officer & SVP, Inventec Inc.
Co-Founder, Skywatch Inc.
chen.wei-chao@inventec.com
For ICPAI 2020, December 2020
2
Inventec Confidential
Smart Manufacturing
Digital twin of factories
Process Automation
o Automatic Optical
Inspection
o …
Predictive Analysis
o …
3
Inventec Confidential
Find Visual Defects on Laptops
How hard can it be?
Customer
Info
Logo
4
Inventec Confidential
Find Visual Defects on Laptops
How hard can it be?
Customer
Info
Logo
5
Inventec Confidential
o S1: Write a fancy diff program
Find Visual Defects on Laptops
How hard can it be?
Golden Defective
6
Inventec Confidential
o S1: Write a fancy diff program
o Variance between capture
Find Visual Defects on Laptops
How hard can it be?
7
Inventec Confidential
o S1: Write a fancy diff program
o Variance between capture
o S2: Use an object detector
Find Visual Defects on Laptops
How hard can it be?
8
Inventec Confidential
Inventec / Skywatch Laptop AOI Machine, v1.0, GTC 2019
9
Inventec Confidential
o S1: Write a fancy diff program
o Variance between capture
o S2: Use an object detector
o Lots of data to label
Find Visual Defects on Laptops
How hard can it be?
10
Inventec Confidential
o S1: Write a fancy diff program
o Variance between capture
o S2: Use an object detector
o Lots of data to label
o S3: Let us capture and label the data anyways
Find Visual Defects on Laptops
How hard can it be?
11
Inventec Confidential
o S1: Write a fancy diff program
o Variance between capture
o S2: Use an object detector
o Lots of data to label
o S3: Let us capture and label the data anyways
o You forgot to look at the sides
Find Visual Defects on Laptops
How hard can it be?
12
Inventec Confidential
o S1: Write a fancy diff program
o Variance between capture
o S2: Use an object detector
o Lots of data to label
o S3: Let us capture and label the data anyways
o You forgot to look at the sides
o S4: Build a proper machine
Find Visual Defects on Laptops
How hard can it be?
13
Inventec Confidential
S4: Build a proper machine
Surely it took a while and a bit of fortune
Inventec Laptop AOI Machine, v2.0, GTC 2020
14
Inventec Confidential
o S1: Write a fancy diff program
o Variance between capture
o S2: Use an object detector
o Lots of data to label
o S3: Let us capture and label the data anyways
o You forgot to look at the sides
o S4: Build a proper machine
o Well, but the product is obsolete before the model is ready
Find Visual Defects on Laptops
How hard can it be?
>6 Months!!
99+% yield, 1000s daily volume
15
Inventec Confidential
o S1: Write a fancy diff program
o Variance between capture
o S2: Use an object detector
o Lots of data to label
o S3: Let us capture and label the data anyways
o You forgot to look at the sides
o S4: Build a proper machine
o Well, but the product is obsolete before the model is ready
o S5: Use less labels with semi-supervised algorithm
Find Visual Defects on Laptops
How hard can it be?
16
Inventec Confidential
Semi-supervised defect detection
TrustMAE, WACV 2021
17
Inventec Confidential
o S1: Write a fancy diff program
o Variance between capture
o S2: Use an object detector
o Lots of data to label
o S3: Let us capture and label the data anyways
o You forgot to look at the sides
o S4: Build a proper machine
o Well, but the product is obsolete before the model is ready
o S5: Use less labels with semi-supervised algorithm
o Fine, but the accuracy is worse than expected
Find Visual Defects on Laptops
How hard can it be?
18
Inventec Confidential
o S1: Write a fancy diff program
o Variance between capture
o S2: Use an object detector
o Lots of data to label
o S3: Let us capture and label the data anyways
o You forgot to look at the sides
o S4: Build a proper machine
o Well, but the product is obsolete before the model is ready
o S5: Use less labels with semi-supervised algorithm
o Fine, but the accuracy is worse than expected
o S6: Wait, let us check if humans are better at it
o Well, turned out they are not
Find Visual Defects on Laptops
How hard can it be?
19
Inventec Confidential
o Passing criteria can vary across inspectors
The Weakest Link
To err is human
Visual
Criteria
20
Inventec Confidential
o Passing criteria can vary across inspectors
o Label quality, acceptance criteria hard to define
The Weakest Link
To err is human
Visual
Criteria
Time
Product
Type
21
Inventec Confidential
o S1: Write a fancy diff program
o Variance between capture
o S2: Use an object detector
o Lots of data to label
o S3: Let us capture and label the data anyways
o You forgot to look at the sides
o S4: Build a proper machine
o Well, but the product is obsolete before the model is ready
o S5: Use less labels with semi-supervised algorithm
o Fine, but the accuracy is worse than expected
o S6: Wait, let us check if humans are better at it
o Well, turned out they are not
o S7: Ok we beat the human, hurray!
Find Visual Defects on Laptops
How hard can it be?
22
Inventec Confidential
o S1: Write a fancy diff program
o Variance between capture
o S2: Use an object detector
o Lots of data to label
o S3: Let us capture and label the data anyways
o You forgot to look at the sides
o S4: Build a proper machine
o Well, but the product is obsolete before the model is ready
o S5: Use less labels with semi-supervised algorithm
o Fine, but the accuracy is worse than expected
o S6: Wait, let us check if humans are better at it
o Well, turned out they are not
o S7: Ok we beat the human, hurray!
o Your machine is too expensive
Find Visual Defects on Laptops
How hard can it be?
23
Inventec Confidential
o Talks
o “Edge AI Smart Manufacturing - Defect Detection and Beyond”, T.
Chen, W-C. Chen, in NVIDIA GTC 2019
o “Toward Taming the Training Data Complexity in Smart
Manufacturing”, D. Tan, H-H. Lee, Y-C. Chen, W-C. Chen, T. Chen,
in NVIDIA GTC 2020
o Papers
o “TrustMAE: A Noise-Resilient Defect Classification Framework
using Memory-Augmented Auto-Encoders with Trust Regions”, D.
Tan, Y-C. Chen, T. Chen, W-C. Chen, in WACV 2021
o “Demystifying Data and AI for Manufacturing: Case Studies from a
Major Computer Maker”, Y-C. Chen et al., in APSIPA Trans 2021.
References
Contact: chen.wei-chao@inventec.com
A Simple AI Problem
That Wasn’t
Wei-Chao Chen 陳維超
Chief Digital Officer & SVP, Inventec Inc.
Co-Founder, Skywatch Inc.
chen.wei-chao@inventec.com
For ICPAI 2020, December 2020

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A Simple AI Problem That Wasn’t: A Smart Manufacturing Case Study

  • 1. A Simple AI Problem That Wasn’t Wei-Chao Chen 陳維超 Chief Digital Officer & SVP, Inventec Inc. Co-Founder, Skywatch Inc. chen.wei-chao@inventec.com For ICPAI 2020, December 2020
  • 2. 2 Inventec Confidential Smart Manufacturing Digital twin of factories Process Automation o Automatic Optical Inspection o … Predictive Analysis o …
  • 3. 3 Inventec Confidential Find Visual Defects on Laptops How hard can it be? Customer Info Logo
  • 4. 4 Inventec Confidential Find Visual Defects on Laptops How hard can it be? Customer Info Logo
  • 5. 5 Inventec Confidential o S1: Write a fancy diff program Find Visual Defects on Laptops How hard can it be? Golden Defective
  • 6. 6 Inventec Confidential o S1: Write a fancy diff program o Variance between capture Find Visual Defects on Laptops How hard can it be?
  • 7. 7 Inventec Confidential o S1: Write a fancy diff program o Variance between capture o S2: Use an object detector Find Visual Defects on Laptops How hard can it be?
  • 8. 8 Inventec Confidential Inventec / Skywatch Laptop AOI Machine, v1.0, GTC 2019
  • 9. 9 Inventec Confidential o S1: Write a fancy diff program o Variance between capture o S2: Use an object detector o Lots of data to label Find Visual Defects on Laptops How hard can it be?
  • 10. 10 Inventec Confidential o S1: Write a fancy diff program o Variance between capture o S2: Use an object detector o Lots of data to label o S3: Let us capture and label the data anyways Find Visual Defects on Laptops How hard can it be?
  • 11. 11 Inventec Confidential o S1: Write a fancy diff program o Variance between capture o S2: Use an object detector o Lots of data to label o S3: Let us capture and label the data anyways o You forgot to look at the sides Find Visual Defects on Laptops How hard can it be?
  • 12. 12 Inventec Confidential o S1: Write a fancy diff program o Variance between capture o S2: Use an object detector o Lots of data to label o S3: Let us capture and label the data anyways o You forgot to look at the sides o S4: Build a proper machine Find Visual Defects on Laptops How hard can it be?
  • 13. 13 Inventec Confidential S4: Build a proper machine Surely it took a while and a bit of fortune Inventec Laptop AOI Machine, v2.0, GTC 2020
  • 14. 14 Inventec Confidential o S1: Write a fancy diff program o Variance between capture o S2: Use an object detector o Lots of data to label o S3: Let us capture and label the data anyways o You forgot to look at the sides o S4: Build a proper machine o Well, but the product is obsolete before the model is ready Find Visual Defects on Laptops How hard can it be? >6 Months!! 99+% yield, 1000s daily volume
  • 15. 15 Inventec Confidential o S1: Write a fancy diff program o Variance between capture o S2: Use an object detector o Lots of data to label o S3: Let us capture and label the data anyways o You forgot to look at the sides o S4: Build a proper machine o Well, but the product is obsolete before the model is ready o S5: Use less labels with semi-supervised algorithm Find Visual Defects on Laptops How hard can it be?
  • 16. 16 Inventec Confidential Semi-supervised defect detection TrustMAE, WACV 2021
  • 17. 17 Inventec Confidential o S1: Write a fancy diff program o Variance between capture o S2: Use an object detector o Lots of data to label o S3: Let us capture and label the data anyways o You forgot to look at the sides o S4: Build a proper machine o Well, but the product is obsolete before the model is ready o S5: Use less labels with semi-supervised algorithm o Fine, but the accuracy is worse than expected Find Visual Defects on Laptops How hard can it be?
  • 18. 18 Inventec Confidential o S1: Write a fancy diff program o Variance between capture o S2: Use an object detector o Lots of data to label o S3: Let us capture and label the data anyways o You forgot to look at the sides o S4: Build a proper machine o Well, but the product is obsolete before the model is ready o S5: Use less labels with semi-supervised algorithm o Fine, but the accuracy is worse than expected o S6: Wait, let us check if humans are better at it o Well, turned out they are not Find Visual Defects on Laptops How hard can it be?
  • 19. 19 Inventec Confidential o Passing criteria can vary across inspectors The Weakest Link To err is human Visual Criteria
  • 20. 20 Inventec Confidential o Passing criteria can vary across inspectors o Label quality, acceptance criteria hard to define The Weakest Link To err is human Visual Criteria Time Product Type
  • 21. 21 Inventec Confidential o S1: Write a fancy diff program o Variance between capture o S2: Use an object detector o Lots of data to label o S3: Let us capture and label the data anyways o You forgot to look at the sides o S4: Build a proper machine o Well, but the product is obsolete before the model is ready o S5: Use less labels with semi-supervised algorithm o Fine, but the accuracy is worse than expected o S6: Wait, let us check if humans are better at it o Well, turned out they are not o S7: Ok we beat the human, hurray! Find Visual Defects on Laptops How hard can it be?
  • 22. 22 Inventec Confidential o S1: Write a fancy diff program o Variance between capture o S2: Use an object detector o Lots of data to label o S3: Let us capture and label the data anyways o You forgot to look at the sides o S4: Build a proper machine o Well, but the product is obsolete before the model is ready o S5: Use less labels with semi-supervised algorithm o Fine, but the accuracy is worse than expected o S6: Wait, let us check if humans are better at it o Well, turned out they are not o S7: Ok we beat the human, hurray! o Your machine is too expensive Find Visual Defects on Laptops How hard can it be?
  • 23. 23 Inventec Confidential o Talks o “Edge AI Smart Manufacturing - Defect Detection and Beyond”, T. Chen, W-C. Chen, in NVIDIA GTC 2019 o “Toward Taming the Training Data Complexity in Smart Manufacturing”, D. Tan, H-H. Lee, Y-C. Chen, W-C. Chen, T. Chen, in NVIDIA GTC 2020 o Papers o “TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions”, D. Tan, Y-C. Chen, T. Chen, W-C. Chen, in WACV 2021 o “Demystifying Data and AI for Manufacturing: Case Studies from a Major Computer Maker”, Y-C. Chen et al., in APSIPA Trans 2021. References Contact: chen.wei-chao@inventec.com
  • 24. A Simple AI Problem That Wasn’t Wei-Chao Chen 陳維超 Chief Digital Officer & SVP, Inventec Inc. Co-Founder, Skywatch Inc. chen.wei-chao@inventec.com For ICPAI 2020, December 2020