Learning to Rank Among Controlled Attributes


Learning to Rank Among Controlled Attributes – To summarize our work with reference to two related problems: (1) how to model the problem of identifying a controlled attribute in high-dimensional data, (2) how to build general artificial learning models for high-dimensional data. We propose a general approach called supervised learning that is able to model the problem of identifying an ordered set of attributes, which results in a very natural and accurate estimation of the control attribute. Using a supervised model, we are able to select the best pair of attributes by applying a simple and accurate estimation algorithm to the problem of the ordering of the attributes. The estimation algorithm is based on a deep neural network. A supervised model is trained on the attribute set, and the learning algorithm learns the classifier for each attribute. The resulting model generalizes to a wide class of supervised learning tasks, such as prediction of the user’s actions, prediction of the user’s choice, and classification of the user’s choice.

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.

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Learning to Rank Among Controlled Attributes

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  • Unsupervised Active Learning with Partial Learning

    Tunneling the Two-level Dynamic Range of Images via Deep LearningDeep 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.


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