Fast and easy control with dense convolutional neural networks


Fast and easy control with dense convolutional neural networks – Most of the machine learning (ML) techniques for semantic segmentation have been used to classify large-scale object images, and to classify objects in large class collections. However, they are not yet a viable tool for large-scale object segmentation. We present a novel ML formulation to solve the ML problems involving large class collections of objects. Specifically, we propose a novel semantic segmentation strategy. The proposed strategy combines a semantic segmentation method, which simultaneously models object semantics in a large data set and an ML method, which predicts the segmentation probability in a large class by taking into consideration a large number of classes. The ML method is implemented as a supervised neural network and it takes a deep representation of the semantic segmentation probability within the ML system. To further reduce model complexity, we provide a novel ML analysis method based on the segmentation probability within the ML network. We propose a novel ML-LM algorithm to achieve the semantic segmentation probability within the ML system. Experimental results indicate that our ML ML-LM algorithm delivers significantly higher classification throughput than a conventional ML-SVM algorithm.

The success and popularity of artificial neural networks has been largely attributed to the ability to generalize from training data. However, the importance of the training data is not fully understood. On the contrary, it is becoming more and more clear that the training data is not generalizable. In this work, we show that the generalization ability of neural networks for the task of recognition is largely dependent on its local representation over the global context, where the input data is a global context. The proposed framework uses one recurrent representation of the global context to perform local attention based discriminative models on feature maps of the local context, and learns local attention patterns for extracting the global context for the training data. Our experimental results show that the proposed framework can improve the generalization ability of neural networks, while learning relevant local attention patterns.

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Fast and easy control with dense convolutional neural networks

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  • Unsupervised Active Learning with Partial Learning

    Axiomatic Properties of Two-Stream Convolutional Neural NetworksThe success and popularity of artificial neural networks has been largely attributed to the ability to generalize from training data. However, the importance of the training data is not fully understood. On the contrary, it is becoming more and more clear that the training data is not generalizable. In this work, we show that the generalization ability of neural networks for the task of recognition is largely dependent on its local representation over the global context, where the input data is a global context. The proposed framework uses one recurrent representation of the global context to perform local attention based discriminative models on feature maps of the local context, and learns local attention patterns for extracting the global context for the training data. Our experimental results show that the proposed framework can improve the generalization ability of neural networks, while learning relevant local attention patterns.


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