# GAN Training Acceleration Using Frechet Descriptor-Based Coreset
## 1 Introduction  
Generative Adversarial Networks (GANs), proposed by [PERSON] [1] in 2014, are a class of deep learning models that are applied to image processing and consist of two sub-networks: a generator and a discriminator. The generator is trained to synthesize artificial versions of the original images, and a discriminator is employed to distinguish the artificial images from the real images. During the GAN training, both the generator network and the discriminator network interact with each other iteratively, resulting in generated images resembling as close as to the real images. GANs have demonstrated state-of-the-art performance in many applications including medicine, such as medical image generation [2], medical image-to-image translation [3], etc. Most recently, CycleGAN [4] has attracted great attention to translate unpaired images from one domain (known as source domain, e.g., one image modality) to another (known as target domain, e.g., a different image modality) by simultaneously training two sets of generators and discriminators, one for each domain. CycleGAN has been applied to organ segmentation [3], tumor detection [5], medical image denoising [6], medical image synthesis [7], blob detection [8], etc. In general, there are two types of CycleGAN-based models: (1) imaging-level translation; (2) object-focused image translation. For imaging-level translation, the model transfers whole images from one domain to another domain on a pixel or voxel basis. For example, [PERSON] et al. [9] proposed a pseudo-3D CycleGAN to synthesize CT images from MRIfor surgical guidance. [PERSON] et al. [10] proposed a switchable CycleGAN with adaptive instance normalization to generate synthesized images for hypopharyngeal cancer diagnosis. For object-focused image translation, the model considers the domain knowledge from the target objects as added constraints to regularize the image translation to alleviate the geometric distortion issue. For example, [PERSON] et al. [11] utilized segmentation to derive a shape consistency constraint and applied it on CycleGAN to solve the shape distortion in cross-modality synthesis. [PERSON] et al. [12] introduced illumination regularization and structure loss function for medical image enhancement. [PERSON] et al. leveraged the geometric information of the target objects to assist the synthetic image rendering [8]. While the performance of CyleGAN-based models in these applications are promising, a common critique is that the model training is slow and often limited by computing resources. For example, the original CycleGAN [4; 13] took 220 h of training with NVIDIA Titan X GPU on 10,000 2D paintings images. The BlobGAN [8], trained on 1000 3D blob images, took \(\sim\)26 h for 50 epochs. One may argue that the training time can be reduced using advanced GPUs. However, such computing resources may not be readily available. To reduce the training time, researchers began exploring training using a subset instead of the whole dataset. Using false positive rate to measure the performance of generated image quality, [PERSON] et al. [14] proposed the DCGAN model and showed the training on subsets can lead to a false positive rate comparable to the training using the entire dataset with less computing time. Unfortunately, it was noted that the performance may be unstable for some experiments, as DCGAN led to very low false positive rate [14]. To maintain the quality of GAN's generated image during subset selection, [PERSON] et al. [15] proposed a novel instance selection approach based on manifold density of dataset. They removed the low-density regions to improve subsets' samples quality. Yet, this assumes that the low-density regions are noisy data region which may not always be true for medical images. Additionally, the computational time of this approach trained on ImageNet was reduced from 14.8 days to only 3.7 days [14], which still is considered to be computationally expensive even with advanced GPU power, for example, eight NVIDIA V100 GPUs in this case.