This content originally appeared on HackerNoon and was authored by Computational Technology for All
:::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
[32×32, 31×31, 30×30,
29×29, 28×28, 27×27,
26×26, 25×25, 24×24,
22×22, 21×21, 20×20,
19×19, 18×18, 17×17,
16×16, 15×15, 14×14,
13×13, 12×12, 11×11,
10×10, 9×9, 8×8,
6×6,5×5, 4×4, 3×3]
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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This content originally appeared on HackerNoon and was authored by Computational Technology for All