Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques
Eugenio Lomurno (Politecnico di Milano),* Andrea Lampis (Politecnico di Milano), Matteo Matteucci (Politecnico di Milano)
The 34th British Machine Vision Conference
Abstract
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. Generative models have emerged as a promising solution for generating synthetic datasets that can replace or augment real-world data. However, the effectiveness of synthetic data is limited by their inability to fully capture the complexity and diversity of real-world data. In this paper, 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 reduces the Classification Accuracy Score gap to an error of 2.03%, 1.78%, 3.99%, 3.33% and 2.04% on the Fashion-MNIST, CIFAR-10, CIFAR-100, CINIC-10 and DermaMNIST 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.Video
Citation
@inproceedings{Lomurno_2023_BMVC,
author = {Eugenio Lomurno and Andrea Lampis and Matteo Matteucci},
title = {Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0715.pdf}
}
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