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While Generative Adversarial Networks (GAN) are popular for their higher sample quality as opposed to other generative models like Variational Autoencoders (VAE) and Boltzmann machines, it is still difficult to evaluate their overall performance. Various aspects must be kept in mind, such as the quality of generated samples, the diversity of classes (within a class and among classes), avoiding overfitting, the use of disentangled latent spaces, agreement of said evaluation metric with human perception, etc. In this paper, we propose a new score, namely, GM Score, which takes into various factors such as sample quality, intra-class and inter-class diversity, overfitting, and other metrics such as precision, recall, and F1 score is employed for discriminability of latent space of Deep Belief Network (DBN) and Restricted Boltzmann Machine (RBM). The evaluation is done for different GANs (GAN, DCGAN, BiGAN, CGAN, CoupledGAN, LSGAN, SGAN, WGAN, and WGAN Improved) trained on the benchmark MNIST dataset. [ABSTRACT FROM AUTHOR]

Existing techniques for hand-written digit recognition (HDR) rely heavily on the hand-coded key points and requires prior knowledge. Training an efficient HDR network with these preconditions is a complicated task. Recently, work on HDR is mainly focused on deep learning (DL) approaches and has exhibited remarkable results. However, effective detection and classification of numerals is still a challenging task due to people's varying writing styles and the presence of blurring, distortion, light and size variations in the input sample. To cope with these limitations, we present an effective and efficient HDR system, introducing a customized faster regional convolutional neural network (Faster-RCNN). This approach comprises three main steps. Initially, we develop annotations to obtain the region of interest. Then, an improved Faster-RCNN is employed in which DenseNet-41 is introduced to compute the deep features. Finally, the regressor and classification layer is used to localize and classify the digits into ten classes. The performance of the proposed method is analyzed on the standard MNIST database, which is diverse in terms of changes in lighting conditions, chrominance, shape and size of digits, and the occurrence of blurring and noise effects, etc. Additionally, we have also evaluated our technique over a cross-dataset scenario to prove its efficacy. Experimental evaluations demonstrate that the approach is more competent and able to accurately detect and classify numerals than other recent methods. [ABSTRACT FROM AUTHOR]