Review: DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

1. DeepGauge: Multi-Granularity Testing
Criteria for Deep Learning Systems
Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue ,
Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, Jianjun Zhao, and
Yadong Wang
will be published @ ASE 2018
Jinhan Kim
2018.8.3

3. How to Test Deep Learning System
Systematically?

4. Not Like Traditional Software

5. Neuron Coverage of DeepXplore

6. Neuron

7. Neuron Coverage
• # of neurons activated / # total neurons
-11
Car
Face
hedge
vedge
...
Nose
Eyes
Wheel
...
...
...
f: ReLU
3
1
2
1
2
3
f(1)
1
Neuron coverage: 4/7=57%
when activation threshold=0.75

8. Fine-grained Neuron Coverage

9. Neuron-Level Coverage
• k-multisection Neuron Coverage
Neuron
Inputs
0.1
0.13
0.17
0.2
0.43
0.15
Outputs K-Section bucketing
low high

10. Neuron-Level Coverage
• Neuron Boundary Coverage
K-Section bucketing
low high
Lower Corner Upper Corner
Neuron

11. Neuron-Level Coverage
• Strong Neuron Activation Coverage
K-Section bucketing
low high
Lower Corner Upper Corner
Neuron

12. Layer-Level Coverage
• Top-k Neuron Coverage
6
7
1
2
3
4
5
0.5
0.2
0.6
Pick Top-2
“Neuron 3 and 5 are
covered”

13. Layer-Level Coverage
• Top-k Neuron Patterns
6
7
1
2
3
4
5
Pick Top-1 for each layer
1 4 7
Pattern

14. Study

15. Dataset and Model

16. Configuration

17. Result: k-multisection Neuron Coverage

18. Result: Strong Neuron Activation Coverage

19. Result: Neuron Boundary Coverage

20. Result: Top-k Neuron Coverage

21. Result: Top-k Neuron Patterns

22. Remarks
• Adversarial test-sets boost coverage but..
• Note that increasing the test coverage does not necessarily
imply that new defects could be detected in traditional software
testing.

23. Comparison with DeepXplore’s NC

24. Result: DeepXplore’s Neuron Coverage (DNC)

25. Why?

26. Investigation on DNC
• DNC uses the same threshold as the activation evaluation for
all the neurons.
• DNC normalizes the dynamic range of neuron outputs
according to max and min output of neurons on the
corresponding layer for each input image under analysis.