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While generative adversarial networks (GAN) are popular for their highersample quality as opposed to other generative models like the variationalautoencoders (VAE) and Boltzmann machines, they suffer from the same difficultyof the evaluation of generated samples. Various aspects must be kept in mind,such as the quality of generated samples, the diversity of classes (within aclass and among classes), the use of disentangled latent spaces, agreement ofsaid evaluation metric with human perception, etc. In this paper, we propose anew score, namely, GM Score, which takes into various factors such as samplequality, disentangled representation, intra-class and inter-class diversity,and other metrics such as precision, recall, and F1 score are employed fordiscriminability of latent space of deep belief network (DBN) and restrictedBoltzmann machine (RBM). The evaluation is done for different GANs (GAN, DCGAN,BiGAN, CGAN, CoupledGAN, LSGAN, SGAN, WGAN, and WGAN Improved) trained on thebenchmark MNIST dataset.

GM Score: Incorporating inter-class and intra-class generator diversity,  discriminability of disentangled representation, and sample fidelity for  evaluating GANs