AugMix:A Simple Data Processing Method to Improve Robustness and Uncertainty 2020.8.5

1. AugMix:
A Simple Data Processing Method to
Improve Robustness and Uncertainty
2020.8.5
Yongsu Baek

2. Data Augmentation
9 basic operations in PIL (Python Image Library)
(autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y, translate_x, translate_y)

3. Composition of Operations
• Diverse Transformations

4. Composition of Operations
• Diverse Transformations
• Unrealistic image (too much steps)

5. Mix!

6. Components
1. Augmentations
• Without corruptions in test data
• Severity (eg. rotate: 2°, -15°)
• Length of chain: 1~3 (uniformly random)
2. Mixing
• Elementwise convex combinations
• Sample mixed images coefficients from Dirichlet(a, …, a)
• Sample skip-connection weight from Beta(a,a)
3. Jensen-Shannon Divergence Consistency Loss
• Enforces smoother neural network responses
• Stochasticity
• the choice of operations, the severity of these operations, the lengths of the
augmentation chains, and the mixing weights

7. Algorithm

8. Objective
• Dataset:
• Training: Cifar100
• Test: Cifar100-C (#corruption =n, n images + 1 label)
• Base Model: WideResNet 40-2
• Task:
1. Compare average classification error between
• AugMix w/ JSD loss of 1/2/3 augmixed image(s)
• AugMix w/o JSD loss
• No AugMix
2. Show some augmented images
3. (optional task) What if change black blank area to
white or random pixel?

9. 참고
• [paper] https://arxiv.org/abs/1912.02781
• [template code] https://github.com/yongsu-bloo/AugMix-pytorch

10. Thank you !
Any Questions ?