Tatsuya Sueki Master thesis

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The document discusses techniques for interpreting and explaining errors in deep neural network object detection models. It describes recent work on feature attribution methods like TCAV that allow quantitative evaluation of what concepts a model has learned. The document also discusses new research on using these interpretation methods to analyze false positives made by object detectors and generating concept-based explanations of errors. Future work directions include developing automated techniques for explanation and reducing false positives.

Science

Explanation and Prediction of False Positives in Object Detection
48-186130

DNN (deep neural network)
(false negative) (false positive)

• (feature attribution)
•
•
•
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[1]: D. Erhan et al. “Visualizing Higher-Layer Features of a Deep Network” University of Montreal 2009
[2]: J. T. Springenberg et al. “Striving for Simplicity: The All Convolutional Net” ICLR (workshop track) 2015
[3]: M. Sundararajan et al. “Axiomatic Attribution for Deep Networks” ICML 2017
[4]: D. Smilkov et al. “SmoothGrad: removing noise by adding noise” arXiv 2017
[1] [2] [3]
[4]

TCAV (Testing with Concept Activation Vectors) [Kim 18]
Contribution:
( )
( )
( )
( =CAV) CAV
→
[Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

TCAV (Testing with Concept Activation Vectors) [Kim 18]
Contribution:
( )
( )
( )
( =CAV) CAV
→
CAV
[Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

ACE (Automatic Concept-based Explanation) [Ghorbani 19]
Contribution:
[Achanta 12]
→
TCAV [Kim 18]
[Ghorbani 19]: A. Ghorbani et al. “Towards Automatic Concept-based Explanations” arXiv 2019
[Achanta 12]: R. Achanta et al. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods” IEEE TPAMI 2012
[Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

bounding box
→
TCAV [Kim 18]
[Achanta 12]
[Ghorbani 19]: A. Ghorbani et al. “Towards Automatic Concept-based Explanations” arXiv 2019
[Achanta 12]: R. Achanta et al. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods” IEEE TPAMI 2012
[Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

→
TCAV [Kim 18]
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bounding box
: 0.8
[Achanta 12]
[Ghorbani 19]: A. Ghorbani et al. “Towards Automatic Concept-based Explanations” arXiv 2019
[Achanta 12]: R. Achanta et al. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods” IEEE TPAMI 2012
[Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

→
TCAV [Kim 18]
0.9
0.2
0.1
: 0.8
bounding box
: 0.7
[Achanta 12]
[Ghorbani 19]: A. Ghorbani et al. “Towards Automatic Concept-based Explanations” arXiv 2019
[Achanta 12]: R. Achanta et al. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods” IEEE TPAMI 2012
[Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

• Benchmark BDD100K [Yu 18]
• person
• CenterNet [Zhou 19], Faster R-CNN [Ren 15]
[Yu 18]: F. Yu et al. “BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling” arXiv 2018
[Zhou 19]: X. Zhou et al. “Objects as Points” arXiv 2019
[Ren 15]: S. Ren et al. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” NeurIPS 2015

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Average Precision (AP)
http://sucrose.hatenablog.com/entry/2017/02/26/224559

[Tao 18]: G. Tao et al. “Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples” NeurIPS 2018
AmI (Attacks meet Interpretability) [Tao 18]
Contribution:

• Variational Autoencoder (VAE)
• J. An and S. Cho “Variational Autoencoder based Anomaly
Detection using Reconstruction Probability” Special Lecture on IE
2015
• D. Park et al. “A Multimodal Anomaly Detector for Robot-Assisted
Feeding Using an LSTM-based Variational Autoencoder” IEEE
Robotics and Automation Letters 2018
•
• P. W. Koh and P. Liang “Understanding Black-box Predictions via
Influence Functions” ICML 2017
•
• C. Yeh et al. “Representer Point Selection for Explaining Deep
Neural Networks” NeurIPS 2018
• logit

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Amount of data is getting bigger in every moment and this data comes from everywhere; social media, sensors, search engines, GPS signals, transaction records, satellites, financial markets, ecommerce sites etc. This large volume of data may be semi-structured, unstructured or even structured. So it is important to derive meaningful information from this huge data set. Clustering is the process to categorize data such that data are grouped in the same cluster when they are similar according to specific metrics. In this paper, we are working on k-mean clustering technique to cluster big data. Several methods have been proposed for improving the performance of the k-means clustering algorithm. We propose a method for making the algorithm less time consuming, more effective and efficient for better clustering with reduced complexity. According to our observation, quality of the resulting clusters heavily depends on the selection of initial centroid and changes in data clusters in the subsequence iterations. As we know, after a certain number of iterations, a small part of the data points change their clusters. Therefore, our proposed method first finds the initial centroid and puts an interval between those data elements which will not change their cluster and those which may change their cluster in the subsequence iterations. So that it will reduce the workload significantly in case of very large data sets. We evaluate our method with different sets of data and compare with others methods as well.

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1. Explanation and Prediction of False Positives in Object Detection 48-186130

2. DNN (deep neural network) (false negative) (false positive)

3. Variational Autoencoder (VAE)

4. • (feature attribution) • • • • [1]: D. Erhan et al. “Visualizing Higher-Layer Features of a Deep Network” University of Montreal 2009 [2]: J. T. Springenberg et al. “Striving for Simplicity: The All Convolutional Net” ICLR (workshop track) 2015 [3]: M. Sundararajan et al. “Axiomatic Attribution for Deep Networks” ICML 2017 [4]: D. Smilkov et al. “SmoothGrad: removing noise by adding noise” arXiv 2017 [1] [2] [3] [4]

5. TCAV (Testing with Concept Activation Vectors) [Kim 18] Contribution: ( ) ( ) ( ) ( =CAV) CAV → [Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

6. TCAV (Testing with Concept Activation Vectors) [Kim 18] Contribution: ( ) ( ) ( ) ( =CAV) CAV → [Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

7. TCAV (Testing with Concept Activation Vectors) [Kim 18] Contribution: ( ) ( ) ( ) ( =CAV) CAV → [Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

8. TCAV (Testing with Concept Activation Vectors) [Kim 18] Contribution: ( ) ( ) ( ) ( =CAV) CAV → CAV [Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

9. TCAV (Testing with Concept Activation Vectors) [Kim 18] Contribution: ( ) ( ) ( ) ( =CAV) CAV → • • • [Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

10. ACE (Automatic Concept-based Explanation) [Ghorbani 19] Contribution: [Achanta 12] → TCAV [Kim 18] [Ghorbani 19]: A. Ghorbani et al. “Towards Automatic Concept-based Explanations” arXiv 2019 [Achanta 12]: R. Achanta et al. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods” IEEE TPAMI 2012 [Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

11. bounding box → TCAV [Kim 18] [Achanta 12] [Ghorbani 19]: A. Ghorbani et al. “Towards Automatic Concept-based Explanations” arXiv 2019 [Achanta 12]: R. Achanta et al. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods” IEEE TPAMI 2012 [Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

12. → TCAV [Kim 18] 0.9 0.2 0.1 bounding box : 0.8 [Achanta 12] [Ghorbani 19]: A. Ghorbani et al. “Towards Automatic Concept-based Explanations” arXiv 2019 [Achanta 12]: R. Achanta et al. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods” IEEE TPAMI 2012 [Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

13. → TCAV [Kim 18] 0.9 0.2 0.1 : 0.8 bounding box : 0.7 [Achanta 12] [Ghorbani 19]: A. Ghorbani et al. “Towards Automatic Concept-based Explanations” arXiv 2019 [Achanta 12]: R. Achanta et al. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods” IEEE TPAMI 2012 [Kim 18]: B. Kim et al. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)” ICML 2018

14. • Benchmark BDD100K [Yu 18] • person • CenterNet [Zhou 19], Faster R-CNN [Ren 15] [Yu 18]: F. Yu et al. “BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling” arXiv 2018 [Zhou 19]: X. Zhou et al. “Objects as Points” arXiv 2019 [Ren 15]: S. Ren et al. “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” NeurIPS 2015

15.

16. 2 7 11 14

17. 14

18. 0.3 0.3 0.3 0.9 0.8 0.6 0.7 0.5 0.5 0.5 0.2 0.1 0.9 0.9

19.

20. 1 1

21. 10.24%

22. • • • • • 10.24% Future Work • 3 • • •

23.

24. 18.23% 10.24% (IoU = 0)

25. Average Precision (AP) http://sucrose.hatenablog.com/entry/2017/02/26/224559

26.

27. [Tao 18]: G. Tao et al. “Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples” NeurIPS 2018 AmI (Attacks meet Interpretability) [Tao 18] Contribution:

28. • Variational Autoencoder (VAE) • J. An and S. Cho “Variational Autoencoder based Anomaly Detection using Reconstruction Probability” Special Lecture on IE 2015 • D. Park et al. “A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder” IEEE Robotics and Automation Letters 2018 • • P. W. Koh and P. Liang “Understanding Black-box Predictions via Influence Functions” ICML 2017 • • C. Yeh et al. “Representer Point Selection for Explaining Deep Neural Networks” NeurIPS 2018 • logit

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