Tunneling the Two-level Dynamic Range of Images via Deep Learning


Tunneling the Two-level Dynamic Range of Images via Deep Learning – Deep Learning (DL) has been successfully applied to images in many different applications. In spite of its success, the current state-of-the-art image recognition system is not yet able to fully utilize the generative techniques. In this paper, we explore the effectiveness of a deep learning approach. The learning is based on a classifier, i.e., the convolutional network, which is trained on image features, to improve the recognition accuracy of the system. We have done this through two different methods, namely a deep feed-forward network and a fully convolutional neural network trained on the input image feature. In this paper, we investigate whether deep learning can be considered robust to the loss of discriminative features. The experimental results show that our deep learning approach achieves comparable or better recognition accuracy compared to one baseline system. We also analyze our proposed method on an RGB-D image and compare it to the state-of-the-art state-of-the-art model.

The problem of stochastic optimization (SMO) of stochastic (or stationary) optimization (SSP) learning of a linear class of variables is approached by proposing an efficient algorithm using (converged) gradient descent. This algorithm involves sampling an unknown Gaussian distribution, and then a parameterized (Gaussian) random function (f-pr) is utilized to estimate the probability of sampling this distribution. This algorithm is a popular extension of the popular multi-armed bandit algorithm that utilizes the posterior distributions. We illustrate the proposed algorithm with a simulation dataset and a detailed analysis of the learning process.

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Tunneling the Two-level Dynamic Range of Images via Deep Learning

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  • Learning to Detect Small Signs from Large Images

    Improving the Robotic Stent Cluster Descriptor with a Parameter-Free ArchitectureThe problem of stochastic optimization (SMO) of stochastic (or stationary) optimization (SSP) learning of a linear class of variables is approached by proposing an efficient algorithm using (converged) gradient descent. This algorithm involves sampling an unknown Gaussian distribution, and then a parameterized (Gaussian) random function (f-pr) is utilized to estimate the probability of sampling this distribution. This algorithm is a popular extension of the popular multi-armed bandit algorithm that utilizes the posterior distributions. We illustrate the proposed algorithm with a simulation dataset and a detailed analysis of the learning process.


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