A Data-centric Approach to Class-specific Bias in Image Data Augmentation: Appendices A-L by@computational
148 reads
A Data-centric Approach to Class-specific Bias in Image Data Augmentation: Appendices A-L
by Computational Technology for AllAugust 31st, 2024
Too Long; Didn't Read
Data augmentation enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly.
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.
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
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
This paper is available on arxiv under CC BY 4.0 DEED license.
L O A D I N G
. . . comments & more!
. . . comments & more!
