# GAN Training Acceleration Using Frechet Descriptor-Based Coreset
## 5 Conclusions  
In this research, an FDD-Coreset approach is proposed to select subset samples used in GAN training to address the computational challenges. A Frechet Descriptor Distance (FDD) is first proposed to measure the difference between each pair of blob images. Second, we select the Coreset from the entire dataset using FDD metric. We conducted two experiments. In the first experiment, we evaluated the performance of the FDD-Coreset on the BlobGAN using a set of 3D synthetic blob images. Computational time and eight performance metrics including Peak Signal-to-Noise Ratio (\(PSNR\)), Detection Error Rate (\(DER\)), Precision, Recall, F-score, Dice coefficient, Intersection over Union (\(IoU\)), and Blobness (\(PB\)), were used to compare the performance of the FDD-Coreset with the entire dataset (1000 random samples) and the IED-Coreset. Compared to training on the entire dataset, the BlobGAN trained on the FDD-Coreset greatly achieved a more than 96% decrease in computational time, a more than 25% increase in \(PSNR\), a more than 40% decrease in \(DER\), and provides comparable performance in blob detection (Precision, Recall, F-score), blob segmentation (Dice, \(IoU\)), and blob synthesis (\(PB\)). Three 3D MR human kidney images were studied in the second experiment. Compared to training on the entire dataset, the BlobGAN trained on the FDD-Coreset greatly achieved a more than 96% decrease in computational time and comparable performance in \(N_{glom}\) and \(aV_{glom}\). From the results of the two experiments, we conclude that the BlobGAN trained on the proposed FDD-Coreset significantly reduces the training time of the BlobGAN, achieves higher denoising performance (\(PSNR\)) and maintains approximate performance of blob identification compared to training on the entire dataset. While the results of this study are encouraging, there is room for improvement. The proposed FDD is derived from object statistics, which are blob descriptors from blob distribution. This could be potentially used for accelerating other object-focused image translation models where the shape of objects (blob, nuclei, glomeruli, etc.) follow Gaussian distribution. However, if the shape of objects (tumor, organ, etc.) is irregular and the distribution is unknown, the proposed FDD metric will not be applicable. It is our plan to explore a distribution-agnostic-based Coreset approach as the next step. In addition, the blob images studied in this research have a large number of objects to derive the statistic descriptors. Some other medical image applications may have a smaller number of objects (e.g., tumors, lesions) to be investigated. We plan to study other discriminative features instead of shape as object statistics, to increase the effectiveness of Coreset. **Author Contributions:** Conceptualization, Y.X. and T.W. ; methodology, Y.X. and T.W. ; validation, Y.X. ; formal analysis, Y.X. ; investigation, Y.X. ; resources, T.W. ; data curation, Y.X. and T.W. ; writing--original draft preparation, Y.X. and T.W. ; writing--review and editing, Y.X., T.W., J.R.C. and K.M.B. ; visualization, Y.X. ; supervision, T.W. ; project administration, T.W. ; funding acquisition, T.W., J.R.C. and K.M.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by funds from the National Institute of Health award under grant number R01 DK110622, R01 DK111861. **Institutional Review Board Statement:** In this research, three human kidneys were obtained after autopsy through a donor network (The International Institute for the Advancement of Medicine, Edison, NJ) after receiving Institutional Review Board (IRB) approval. Details of IRB are in [19]. **Informed Consent Statement:** Informed consent was obtained from Arizona State University and details are in [19]. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author.