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An Evaluation of Approaches for Generative Adversarial Network Overfitting Detection

thesis
posted on 2023-11-20, 19:27 authored by Tung Tien VuTung Tien Vu

Generating images from training samples solves the challenge of imbalanced data. It provides the necessary data to run machine learning algorithms for image classification, anomaly detection, and pattern recognition tasks. In medical settings, having imbalanced data results in higher false negatives due to a lack of positive samples. Generative Adversarial Networks (GANs) have been widely adopted for image generation. GANs allow models to train without computing intractable probability while producing high-quality images. However, evaluating GANs has been challenging for the researchers due to a need for an objective function. Most studies assess the quality of generated images and the variety of classes those images cover. Overfitting of training images, however, has received less attention from researchers. When the generated images are mere copies of the training data, GAN models will overfit and will not generalize well. This study examines the ability to detect overfitting of popular metrics: Maximum Mean Discrepancy (MMD) and Fréchet Inception Distance (FID). We investigate the metrics on two types of data: handwritten digits and chest x-ray images using Analysis of Variance (ANOVA) models.

History

Degree Type

  • Doctor of Philosophy

Department

  • Computer and Information Technology

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

John Springer

Additional Committee Member 2

Sudip Vhaduri

Additional Committee Member 3

Romila Pradhan

Additional Committee Member 4

Kathryn Seigfried-Spellar

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