Object Detection Using Deep Learning – With the explosion in the size and sophistication of modern 3D images, most of the tasks associated with object detection have to focus on image segmentation. In this work, we propose a method to exploit the 3D geometry and shape data to detect objects from natural images in a supervised and natural environment. This provides a framework for automatically segmenting objects in large images. The segmentation is performed using a deep convolutional convolutional neural network (CNN) and a 3D convolutional neural network (CNN-DNN). Our approach performs fine-tuning and visualizations with the goal of understanding objects in a large-scale scenario. We show that our CNN-DNN approach can easily generate object classes with more than 20% spatial precision, surpassing state-of-the-art approaches on a benchmark dataset.
Learning a large class of discriminative features with high similarity is considered. This paper aims at improving the performance of many-class classification algorithm with high similarity. The problem is first addressed by means of neural network. A recurrent neural network model is constructed for a given classification task. The model is trained using a set of discriminative features which are drawn from the set of discriminative features at each training step. In order to extract features related with the task, the model has to learn a set of different discriminative features at each training step. As well as this, a Bayesian network is used to jointly learn the discriminative features at each step. Then the model can learn discriminative features with high similarity to obtain a lower bound value of the bound of the similarity measure. Experimental results on the MNIST dataset show that the proposed method improves classification performance compared to other state-of-the-art deep-learning-based discriminative models.
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Optimal cost for error: a deterministic outcome functionLearning a large class of discriminative features with high similarity is considered. This paper aims at improving the performance of many-class classification algorithm with high similarity. The problem is first addressed by means of neural network. A recurrent neural network model is constructed for a given classification task. The model is trained using a set of discriminative features which are drawn from the set of discriminative features at each training step. In order to extract features related with the task, the model has to learn a set of different discriminative features at each training step. As well as this, a Bayesian network is used to jointly learn the discriminative features at each step. Then the model can learn discriminative features with high similarity to obtain a lower bound value of the bound of the similarity measure. Experimental results on the MNIST dataset show that the proposed method improves classification performance compared to other state-of-the-art deep-learning-based discriminative models.
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