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. 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. Authors: 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 Abstract and 1 Introduction Abstract and 1 Introduction 2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture 2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture 2.1 Data Augmentation Robustness Scouting 2.1 Data Augmentation Robustness Scouting 2.2 The Specifics Of Data Affect Augmentation-Induced Bias 2.2 The Specifics Of Data Affect Augmentation-Induced Bias 2.3 Adding Random Horizontal Flipping Contributes To 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 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias 3 Conclusion and Limitations, and References 3 Conclusion and Limitations, and References Appendices A-L 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: Without Random Horizontal Flip: With 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. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

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

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

A Data-centric Approach to Class-specific Bias in Image Data Augmentation: Abstract and Intro

Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias

A Data-centric Approach to Class-specific Bias in Image Data Augmentation: Abstract and Intro

The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization

Alternative Architectures Have Variable Effect On Augmentation-Induced Bias

A Data-centric Approach to Class-specific Bias in Image Data Augmentation: Conclusion and Limitation

A Data-centric Approach to Class-specific Bias in Image Data Augmentation: Abstract and Intro

Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias

A Data-centric Approach to Class-specific Bias in Image Data Augmentation: Abstract and Intro

The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization

Alternative Architectures Have Variable Effect On Augmentation-Induced Bias

A Data-centric Approach to Class-specific Bias in Image Data Augmentation: Conclusion and Limitation

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps