Actual Impact of GAN Augmentation on CNN Classification Performance

Abstract

In industrial inspection settings, it is common that data is either hard or expensive to acquire. Generative modeling offers a way to reduce those costs by filling out scarce training data sets automatically. Generative Adversarial Networks (GANs) have shown incredible results in the field of artificial image data generation, but until recently were not ready for industrial applications, because of unclear performance metrics and instabilities. However, with the introduction of Wasserstein GAN, which comprises an interpretable loss metric and general stability, it is promising to try using those algorithms for industrial classification tasks. Therefore, we present a case study on a single digit image classification task of banknote serial numbers, where we simulate use cases with missing data. For those selected situations, different data generation algorithms were implemented incorporating GANs in various ways to augment scarce training data sets. As a measure of plausibility of those artificially generated data, we used the classification performance of a CNN trained on them. We analyzed the gains in classification accuracy when augmenting the training samples with GAN images and compare them to results with either more classically generated, rendered artificial data and near perfect training data situations, respectively.