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In the machine learning (ML) paradigm, data augmentation serves as a regularization approach for creating ML models. The increase in the diversification of training samples increases the generalization capabilities, which enhances the prediction performance of classifiers when tested on unseen examples. Deep learning (DL) models have a lot of parameters, and they frequently overfit. Effectively, to avoid overfitting, data plays a major role to augment the latest improvements in DL. Nevertheless, reliable data collection is a major limiting factor. Frequently, this problem is undertaken by combining augmentation of data, transfer learning, dropout, and methods of normalization in batches. In this paper, we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning (RMDL) which uses the association approaches of multi-DL to yield random models for classification. We present a methodology for using Generative Adversarial Networks (GANs) to generate images for data augmenting. Through experiments, we discover that samples generated by GANs when fed into RMDL improve both accuracy and model efficiency. Experimenting across both MNIST and CIAFAR-10 datasets show that, error rate with proposed approach has been decreased with different random models. [ABSTRACT FROM AUTHOR]

Neural networks have several useful applications in machine learning. However, benefiting from the neural-network architecture can be tricky in some instances due to the large number of parameters that can influence performance. In general, given a particular dataset, a data scientist cannot do much to improve the efficiency of the model. However, by tuning certain hyperparameters, the model's accuracy and time of execution can be improved. Hence, it is of utmost importance to select the optimal values of hyperparameters. Choosing the optimal values of hyperparameters requires experience and mastery of the machine learning paradigm. In this paper, neural network-based architectures are tested based on altering the values of hyperparameters for handwritten-based digit recognition. Various neural network-based models are used to analyze different aspects of the same, primarily accuracy based on hyperparameter values. The extensive experimentation setup in this article should, therefore, provide the most accurate and time-efficient solution models. Such an evaluation will help in selecting the optimized values of hyperparameters for similar tasks. [ABSTRACT FROM AUTHOR]

Crowd counting is an active research area within scene analysis. Over the last 20 years, researchers proposed various algorithms for crowd counting in real-time scenarios due to many applications in disaster management systems, public events, safety monitoring, and so on. In our paper, we proposed an end-to-end semantic segmentation framework for crowd counting in a dense crowded image. Our proposed framework was based on semantic scene segmentation using an optimized convolutional neural network. The framework successfully highlighted the foreground and suppressed the background part. The framework encoded the high-density maps through a guided attention mechanism system. We obtained crowd counting through integrating the density maps. Our proposed algorithm classified the crowd counting in each image into groups to adapt the variations occurring in crowd counting. Our algorithm overcame the scale variations of a crowded image through multi-scale features extracted from the images. We conducted experiments with four standard crowd-counting datasets, reporting better results as compared to previous results. [ABSTRACT FROM AUTHOR]

Accurate face segmentation strongly benefits the human face image analysis problem. In this paper we propose a unified framework for face image analysis through end-to-end semantic face segmentation. The proposed framework contains a set of stack components for face understanding, which includes head pose estimation, age classification, and gender recognition. A manually labeled face data-set is used for training the Conditional Random Fields (CRFs) based segmentation model. A multi-class face segmentation framework developed through CRFs segments a facial image into six parts. The probabilistic classification strategy is used, and probability maps are generated for each class. The probability maps are used as features descriptors and a Random Decision Forest (RDF) classifier is modeled for each task (head pose, age, and gender). We assess the performance of the proposed framework on several data-sets and report better results as compared to the previously reported results. [ABSTRACT FROM AUTHOR]