Self-supervised learning performance

1. Using Self-Supervised Learning
Can Improve Model Robustness
and Uncertainty
arXIV: 2019

2. Concerns for robustness and uncertainty
• Robustness to Common Corruptions
• Robustness to Adversarial Perturbations
• Robustness to Label Corruptions
• Out-of-Distribution Detection
• Conclusion

3. Predict the relative
position of image
patches
Use Resulting representation to
improve object detection
Self supervision for learning without labelled data

4. Related works

5. Create surrogate
classes
• Train on by transforming seed
image patches
• E.g., predict image rotations;

6. Using colorization
as a proxy task;
(Pretext)

7. Maximizing mutual
information
• Features extracted from
multiple views of a shared
context

8. Robustness
Resistant across a variety of
imperfect training and testing
• Fog
• Blur
• JPEG Compression
• Adversarial attack
• Corrupted labels

9. Examples: Robustness

10. Out-of-distribution detection
Anomalous or significantly different data used in the training

11. Robustness to
common corruption
• Noise | Blur | Weather | Digital (Corruption categories)

12. Proposed method: Robustness to rotation prediction
• Auxiliary self-supervision in the
form of predicting rotations

13. Self-supervision with
rotation prediction
• Supervised classification (Texture
biased)
• Self-supervision with rotation
prediction
• Provided Strong regularization to
correct bias
• Concentrate on global structure

14. Results: 19 corruption categories

15. Robustness to Adversarial Perturbations

17. Robustness to label
corruptions
After performing Automatic Labeling/Non-expert labeling

18. Robustness to label
corrupution
• The Gold Loss Correction (GLC) is a semi-verified method for label
noise robustness in deep learning classifiers.

19. Out-of-distribution
detection
• Experiments with anomalies: Gaussian, Rademacher, Blobs,
Textures, SVHN, Places365, LSUN, and CIFAR-100 images.

20. Ablation study with
Imagenet
• Self-attention is useful in one-class OOD detection, enabling
the network to more easily learn shape and compare regions
across the whole image.

21. Self-attention with convolution block attention Module(CBAM)

22. Conclusion
• Rotation prediction can improve classifier robustness to common
corruptions, adversarial perturbations, and label corruptions
• Helpful OOD detection
• OOD detection with large image size (224*224)
• Self attention is of great value in learning global structure