  Acquiring and annotating suitable datasets for training deep learning models
is challenging. This often results in tedious and time-consuming efforts that
can hinder research progress. However, generative models have emerged as a
promising solution for generating synthetic datasets that can replace or
augment real-world data. Despite this, the effectiveness of synthetic data is
limited by their inability to fully capture the complexity and diversity of
real-world data. To address this issue, we explore the use of Generative
Adversarial Networks to generate synthetic datasets for training classifiers
that are subsequently evaluated on real-world images. To improve the quality
and diversity of the synthetic dataset, we propose three novel post-processing
techniques: Dynamic Sample Filtering, Dynamic Dataset Recycle, and Expansion
Trick. In addition, we introduce a pipeline called Gap Filler (GaFi), which
applies these techniques in an optimal and coordinated manner to maximise
classification accuracy on real-world data. Our experiments show that GaFi
effectively reduces the gap with real-accuracy scores to an error of 2.03%,
1.78%, and 3.99% on the Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets,
respectively. These results represent a new state of the art in Classification
Accuracy Score and highlight the effectiveness of post-processing techniques in
improving the quality of synthetic datasets.
