Deep Neural Network Local Pattern Network

CNNs are a specialized class of deep neural networks that have been successfully applied to image and time-series data .CNN models are widely used in various problem areas, e.g., classification 2, 3, pattern recognition 4, 5, segmentation 6, 7, and others, providing state-of-the-art results.The CNN architecture generally comprises convolution, activation, and pooling layers.

In recent years, convolutional neural networks CNN have been promising in the classification of HSI. Although in literature Gabor features are used as the input of deep models, it seems that the performance of CNN can be improved by two-stage textural features based on local binary patterns of Gabor features.

Memory and computation efficient deep learning architec- tures are crucial to continued proliferation of machine learning capabili- ties to new platforms and systems. Binarization of operations in convo- lutional neural networks has shown promising results in reducing model size and computing efficiency. In this paper, we tackle the problem us- ing a strategy different from the existing

3. Local Binary Pattern Network In LBPNet, the forward propagation is composed of two key procedures the LBP operation and channel fusion. In this section, we elaborate on them, describe the carefully designed network structure of LBPNet, and present a back-of-the-envelope calculation of hardware gains of LBPNet. 3.1. LBP Kernel and Operation

Convolutional Neural Networks CNN have had a notable impact on many applications. Modern CNN architectures such as AlexNet , VGG , GoogLetNet , and ResNet have greatly advanced the use of deep learning techniques into a wide range of computer vision applications 4, 19.These gains have surely benefited from the continuing advances in computing and storage capabilities of

Neural Networks CNN 22 have achieved state-of-the-art performance on these OCR tasks 35. As deep learn-ing DL models evolve and take on increasingly complex pattern recognition tasks, they, however, demand tremen-dous computational resources with correspondingly higher performance machines and accelerators that continue to be

This paper proposes an architecture based on the improved local binary pattern LBP shallow deep convolution neural network, which integrates hand-crafted feature pre-processing and the advantage of character learning in the supervised high-level function of CNN, in order to enhance its performance.

Fig.1. Visual Pattern Mining with Deep Neural Network Convolution Neural Network CNN can also be seen as a form of visual pattern mining. CNNs have recently shown to exhibit extraordinary power for visual recognition. The breakthrough performance on large-scale image classi - cation challenges 16 25 is just one example. Many researchers

Deep learning is well known as a method to extract hierarchical representations of data. In this paper a novel unsupervised deep learning based methodology, named Local Binary Pattern Network

Deep convolutional neural networks CNNs have brought breakthroughs in processing clinical electrocardiograms ECGs, speaker-independent speech and complex images. However, typical CNNs require a fixed input size while it is common to process variable-size data in practical use. Recurrent networks such as long short-term memory LSTM are capable of eliminating the restriction, but suffer