Comparative Study on the Deep Learning Algorithms

Deep Learning methods have paved the way for elevating the future technology that is capable of changing the world. In modern times, size of data is increasing with the level of application. Deep learning enables the huge dataset to process the highly optimized algorithms with high accuracy as well as within low time. The network architecture of deep learning works similar to human brain nerves. The network accepts the input dataset and convert the data into matrix form that passed through multiple layers in which, each layer upgrade the data to deliver the prediction or classification at the end. Researchers explored the numerous deep learning models that portrayed an inspiration for developers and benefitted in the field of voice recognition, language translation, image categorization, stock market prediction etc. The concern behind the model is to effectively resolve the numerous tasks which need to distributed representation and human intelligence. The highly advanced processors like CPU and GPU has too enhanced the deep learning application through fast matrix calculations and image processing. We will take the sample of wind dataset and used it for comparing the different Deep Neural Network (DNN) artificial algorithm. Keywords: Analysis, comparison, deep learning, training, prediction.

A Latent Variable Augmentation Method for Image Categorization with Insufficient Training Samples

Over the past few years, we have made great progress in image categorization based on convolutional neural networks (CNNs). These CNNs are always trained based on a large-scale image data set; however, people may only have limited training samples for training CNN in the real-world applications. To solve this problem, one intuition is augmenting training samples. In this article, we propose an algorithm called Lavagan ( La tent V ariables A ugmentation Method based on G enerative A dversarial N ets) to improve the performance of CNN with insufficient training samples. The proposed Lavagan method is mainly composed of two tasks. The first task is that we augment a number latent variables (LVs) from a set of adaptive and constrained LVs distributions. In the second task, we take the augmented LVs into the training procedure of the image classifier. By taking these two tasks into account, we propose a uniform objective function to incorporate the two tasks into the learning. We then put forward an alternative two-play minimization game to minimize this uniform loss function such that we can obtain the predictive classifier. Moreover, based on Hoeffding’s Inequality and Chernoff Bounding method, we analyze the feasibility and efficiency of the proposed Lavagan method, which manifests that the LV augmentation method is able to improve the performance of Lavagan with insufficient training samples. Finally, the experiment has shown that the proposed Lavagan method is able to deliver more accurate performance than the existing state-of-the-art methods.

Fine-Grained Visual Computing Based on Deep Learning

With increasing amounts of information, the image information received by people also increases exponentially. To perform fine-grained categorization and recognition of images and visual calculations, this study combines the Visual Geometry Group Network 16 model of convolutional neural networks and the vision attention mechanism to build a multi-level fine-grained image feature categorization model. Finally, the TensorFlow platform is utilized to simulate the fine-grained image classification model based on the visual attention mechanism. The results show that in terms of accuracy and required training time, the fine-grained image categorization effect of the multi-level feature categorization model constructed by this study is optimal, with an accuracy rate of 85.3% and a minimum training time of 108 s. In the similarity effect analysis, it is found that the chi-square distance between Log Gabor features and the degree of image distortion show a strong positive correlation; in addition, the validity of this measure is verified. Therefore, through the research in this study, it is found that the constructed fine-grained image categorization model has higher accuracy in image recognition categorization, shorter training time, and significantly better performance in similar feature effects, which provides an experimental reference for the visual computing of fine-grained images in the future.

Entropy-based Variational Learning of Finite Inverted Beta-Liouville Mixture Model

Mixture models are a common unsupervised learning technique that have been widely used to statistically approximate and analyse heterogenous data. In this paper, an effective mixture model-based approach for positive vectors clustering and modeling is proposed. Our mixture model is based on the inverted Beta-Liouville (IBL) distribution. To deploy the proposed model, we introduce an entropy-based variational inference algorithm. The performance of the proposed model is evaluated on two real-world applications, namely, human activity recognition and image categorization.

A Practical Approach of Image Categorization

In the previous few years, there has been first-rate boom in the utilization of digital images. Users can now get admission to thousands and thousands of photos, a reality that poses the want for having strategies that can efficaciously and successfully search the visible records of interest. Image categorization and awareness have lengthy been related in the imaginative and prescient literature & studied in Computer Vision with a massive wide variety of options have been proposed. We prolonged the single-image mannequin to strategy the extra difficult issues of simultaneous categorization and focus of a whole image collection, with restricted or no supervision. We determined that sharing data about the structure and look of a phase throughout a series of photos of objects belonging to the identical class can enhance performance.