Generative Adversarial Networks (GANs) have high computational costs to train
their complex architectures. Throughout the training process, GANs' output is
analyzed qualitatively based on the loss and synthetic images' diversity and
quality. Based on this qualitative analysis, training is manually halted once
the desired synthetic images are generated. By utilizing an early stopping
criterion, the computational cost and dependence on manual oversight can be
reduced yet impacted by training problems such as mode collapse,
non-convergence, and instability. This is particularly prevalent in biomedical
imagery, where training problems degrade the diversity and quality of synthetic
images, and the high computational cost associated with training makes complex
architectures increasingly inaccessible. This work proposes a novel early
stopping criteria to quantitatively detect training problems, halt training,
and reduce the computational costs associated with synthesizing biomedical
images. Firstly, the range of generator and discriminator loss values is
investigated to assess whether mode collapse, non-convergence, and instability
occur sequentially, concurrently, or interchangeably throughout the training of
GANs. Secondly, utilizing these occurrences in conjunction with the Mean
Structural Similarity Index (MS-SSIM) and Fr\'echet Inception Distance (FID)
scores of synthetic images forms the basis of the proposed early stopping
criteria. This work helps identify the occurrence of training problems in GANs
using low-resource computational cost and reduces training time to generate
diversified and high-quality synthetic images.

本研究提出了一种新颖的早停准则，通过定量检测训练问题来减少 GAN 的计算成本，生成多样化和高质量的生物医学图像。