A Data-centric Approach to Class-specific Bias in Image Data Augmentation: Appendices A-L

Table of Links

• Abstract and 1 Introduction
• 2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture
• 2.1 Data Augmentation Robustness Scouting
• 2.2 The Specifics Of Data Affect Augmentation-Induced Bias
• 2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias
• 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias
• 3 Conclusion and Limitations, and References
• Appendices A-L

Appendices

Appendix A: Image dimensions (in pixels) off training images after being randomly cropped and before being resized

Appendix B: Dataset samples corresponding to the Fashion-MNIST segment used in training

Appendix C: Dataset samples corresponding to the CIFAR-10 segment used in training

Appendix D: Dataset samples corresponding to the CIFAR-100 segment used in training

Appendix E: Full collection of class accuracy plots for CIFAR-100

Appendix F: Full collection of best test performances for CIFAR100

Without Random Horizontal Flip:

With Random Horizontal Flip

Appendix G: Per-class and overall test set performances samples for the Fashion-MNIST + ResNet50 + Random Cropping + Random Horizontal Flip experiment

Appendix H: Per-class and overall test set performances samples for the CIFAR-10 + ResNet50 + Random Cropping + Random Horizontal Flip experiment

Appendix I: Per-class and overall test set performances samples for the Fashion-MNIST + EfficientNetV2S + Random Cropping + Random Horizontal Flip experiment

Appendix J: Per-class and overall test set performances samples for the Fashion-MNIST + ResNet50 + Random Cropping experiment

Appendix K: Per-class and overall test set performances samples for the CIFAR-10 + ResNet50 + Random Cropping experiment

Appendix L: Per-class and overall test set performances samples for the Fashion-MNIST + SWIN Transformer + Random Cropping + Random Horizontal Flip experiment