CNNs: Neural Network Based Convolutional Partitioning for Image Recognition – Most image analysis methods typically assume that a scene is a collection of images of a specific object and the object, in particular, an object of interest. Many different image analysis techniques are available nowadays and most algorithms require a large amount of expensive processing budget to perform. For these approaches, the task of image recognizer is typically to detect the appearance of a scene from multiple views using a feature learned from images. In this work, we propose a neural network classifier that uses pixel-wise and spatial information while recognizing objects within a set of views from the world while simultaneously learning a pixel-wise image representation for each object, known as a scene. In this work we employ LSTM for object recognition to obtain a better representation for both scene appearance and perception than a linear method. The proposed method is evaluated on three challenging datasets: 3D and 2D. The results indicate that our approach outperforms both linear and linear classification approaches.

We propose a novel CNN architecture for sparse and multivariate sequential learning which has the following properties: It combines recent techniques in sparse learning with recently proposed techniques for multinomial sequential learning. It further improves the performance in the context of sequential learning, as it can adaptively choose between consecutive data points, where data points are drawn from different scales, and thus, it can avoid learning time constraints of data in the training set. The proposed method, which we call Deep CNN, uses both feature maps and feature spaces from the multivariate matrix representation. Moreover, it incorporates the spatial-scale-invariant features from the multivariate structure of the matrix matrix in order to perform sparse inference in time-based temporal networks, i.e., the temporal network with spatial-scale covariates. We experiment the approach with both synthetic observations and a dataset created by a real application of neural networks, which shows that it achieves a comparable approximation ratio than the existing state-of-the-arts in terms of training time and memory efficiency.

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# CNNs: Neural Network Based Convolutional Partitioning for Image Recognition

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Compression of Deep Convolutional Neural Networks on GPU for FPGAWe propose a novel CNN architecture for sparse and multivariate sequential learning which has the following properties: It combines recent techniques in sparse learning with recently proposed techniques for multinomial sequential learning. It further improves the performance in the context of sequential learning, as it can adaptively choose between consecutive data points, where data points are drawn from different scales, and thus, it can avoid learning time constraints of data in the training set. The proposed method, which we call Deep CNN, uses both feature maps and feature spaces from the multivariate matrix representation. Moreover, it incorporates the spatial-scale-invariant features from the multivariate structure of the matrix matrix in order to perform sparse inference in time-based temporal networks, i.e., the temporal network with spatial-scale covariates. We experiment the approach with both synthetic observations and a dataset created by a real application of neural networks, which shows that it achieves a comparable approximation ratio than the existing state-of-the-arts in terms of training time and memory efficiency.