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

:::info Authors:

(1) Athanasios Angelakis, Amsterdam University Medical Center, University of Amsterdam - Data Science Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands

(2) Andrey Rass, Den Haag, Netherlands.

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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

[32x32, 31x31, 30x30,

29x29, 28x28, 27x27,

26x26, 25x25, 24x24,

22x22, 21x21, 20x20,

19x19, 18x18, 17x17,

16x16, 15x15, 14x14,

13x13, 12x12, 11x11,

10x10, 9x9, 8x8,

6x6,5x5, 4x4, 3x3]

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

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Appendix F: Full collection of best test performances for CIFAR100

Without Random Horizontal Flip:

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\ With Random Horizontal Flip

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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

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:::info This paper is available on arxiv under CC BY 4.0 DEED license.

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