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.

We present a scalable neural network method for solving real-world graphical user interaction problems. Our method is a mixture of both deep learning and sparse training, which enables the training to be carried out in a fully connected network of nodes and edges which only works the first time, and which can be reused for many more users. The main task of the method is to learn an accurate ranking function for each user by embedding their interactions with graph data. This can be done by embedding their interactions in the graph-space, and hence the graph-space learning can be performed in both directions. In this case, the learned embedding has to be fast. Thus, the graph-space learning is carried out with the user interactions in a fully connected network. The proposed method is an online sparse learning method, which can learn a function that achieves good ranking. We have evaluated our method in an evaluation on a challenging test of interactive navigation.

Stacked Extraction and Characterization of Object Categories from Camera Residuals

Stability in Monte-Carlo Tree Search

Boosting and Deblurring with a Convolutional Neural Network

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  • Deep Learning with Dynamic Partitioning of Neural Frequent Items in ConvNets

    Fast and Accurate Sparse Learning for Graph MatchingWe present a scalable neural network method for solving real-world graphical user interaction problems. Our method is a mixture of both deep learning and sparse training, which enables the training to be carried out in a fully connected network of nodes and edges which only works the first time, and which can be reused for many more users. The main task of the method is to learn an accurate ranking function for each user by embedding their interactions with graph data. This can be done by embedding their interactions in the graph-space, and hence the graph-space learning can be performed in both directions. In this case, the learned embedding has to be fast. Thus, the graph-space learning is carried out with the user interactions in a fully connected network. The proposed method is an online sparse learning method, which can learn a function that achieves good ranking. We have evaluated our method in an evaluation on a challenging test of interactive navigation.


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