  Microplastic particle ingestion or inhalation by humans is a problem of
growing concern. Unfortunately, current research methods that use machine
learning to understand their potential harms are obstructed by a lack of
available data. Deep learning techniques in particular are challenged by such
domains where only small or imbalanced data sets are available. Overcoming this
challenge often involves oversampling underrepresented classes or augmenting
the existing data to improve model performance. This paper proposes GANsemble:
a two-module framework connecting data augmentation with conditional generative
adversarial networks (cGANs) to generate class-conditioned synthetic data.
First, the data chooser module automates augmentation strategy selection by
searching for the best data augmentation strategy. Next, the cGAN module uses
this strategy to train a cGAN for generating enhanced synthetic data. We
experiment with the GANsemble framework on a small and imbalanced microplastics
data set. A Microplastic-cGAN (MPcGAN) algorithm is introduced, and baselines
for synthetic microplastics (SYMP) data are established in terms of Frechet
Inception Distance (FID) and Inception Scores (IS). We also provide a synthetic
microplastics filter (SYMP-Filter) algorithm to increase the quality of
generated SYMP. Additionally, we show the best amount of oversampling with
augmentation to fix class imbalance in small microplastics data sets. To our
knowledge, this study is the first application of generative AI to
synthetically create microplastics data.
