Boosting and Deblurring with a Convolutional Neural Network


Boosting and Deblurring with a Convolutional Neural Network – Feature extraction and classification are two important applications of machine learning in computer vision. In this work, we propose a novel deep convolutional neural network architecture called RNN-CNet to automatically train image classifiers. The RNN architecture is based on a CNN architecture, and is capable of handling the state-of-the-art convolutional neural networks. We demonstrate that the RNN-CNet is much more robust to the amount of labeled data than their CNN counterparts, with the advantage being that it can easily provide a compact representation of the class, which could be easily adapted for various applications. We also present a novel feature extraction technique to automatically predict the appearance of the objects that they occlude. The proposed approach is also evaluated on the task of object object pose estimation, and outperforms all other supervised CNN based methods on both benchmark and real-world datasets. We further demonstrate that the proposed feature extraction method outperforms all state-of-the-art CNN based model choices in three challenging datasets.

In this paper, we present a new classification method based on non-Gaussian conditional random fields. As a consequence, the non-Gaussian conditional random field (NB-Field) has many different useful properties, as it can be used to predict the true state of a function by either predicting the model or predicting the model itself from data. Furthermore, the non-Gaussian conditional random field can be used as a model in a supervised setting. Specifically, the non-Gaussian conditional random field can be used as a supervised model for classifying a single point, and thus a non-Gaussian conditional random field is also used to evaluate the accuracy of a function predicting a conditional parameter estimation (which the conditional parameter estimation model is in the supervised setting). The method based on the non-Gaussian conditional random field has also been applied to the multi-class classification problem. Our results show that the NB-Field has a superior classification performance compared to the conditional random field, while the two models are not equally correlated.

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Boosting and Deblurring with a Convolutional Neural Network

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  • A Convex Programming Approach to Multilabel Classification

    Machine Learning for the Classification of High Dimensional Data With Partial InferenceIn this paper, we present a new classification method based on non-Gaussian conditional random fields. As a consequence, the non-Gaussian conditional random field (NB-Field) has many different useful properties, as it can be used to predict the true state of a function by either predicting the model or predicting the model itself from data. Furthermore, the non-Gaussian conditional random field can be used as a model in a supervised setting. Specifically, the non-Gaussian conditional random field can be used as a supervised model for classifying a single point, and thus a non-Gaussian conditional random field is also used to evaluate the accuracy of a function predicting a conditional parameter estimation (which the conditional parameter estimation model is in the supervised setting). The method based on the non-Gaussian conditional random field has also been applied to the multi-class classification problem. Our results show that the NB-Field has a superior classification performance compared to the conditional random field, while the two models are not equally correlated.


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