Deep Learning-Based Facial Search

Deep Learning-Based Facial Search – While the past years have witnessed a dramatic increase of interest in recognizing face images, there have been a number of challenges in this field. To address these challenges, we address the problem of the deep learning based face search. In this work, we propose a novel deep learning based face search method for different purposes, called face recognition based face search. Given the recent success of Face Recognition, our method can be used for the training of supervised and unsupervised face database. While the performance of such deep learning based face search methods is usually comparable to that of unsupervised face search, we show that our approach achieves higher classification accuracy than supervised face search methods while being able to perform comparable tasks to unsupervised face search.

We present a new statistical approach for learning Bayesian network models, based on a linear-diagonal model and a supervised approach for learning stochastic Bayesian networks. A model is learned as a feature graph over the data points and the training sample of the model is fitted to the data and its expected distributions in the feature space. The proposed approach addresses both the choice of model parameters and the selection of the parameters themselves. The choice of model parameters was determined by the Bayesian model’s predictions as a function of the data and the data set size, hence it was necessary to choose a new parameter to calculate the expected distribution of the parameters over the data set size. We show that the proposed method can be used in many other computer vision tasks, such as object categorization, video summarization, image classification, and learning from low dimensional data, and it is applicable to these applications.

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Deep Learning-Based Facial Search

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  • Adaptive Stochastic Learning

    A Survey of Optimizing Binary Mixed-Membership Stochastic BlockmodelsWe present a new statistical approach for learning Bayesian network models, based on a linear-diagonal model and a supervised approach for learning stochastic Bayesian networks. A model is learned as a feature graph over the data points and the training sample of the model is fitted to the data and its expected distributions in the feature space. The proposed approach addresses both the choice of model parameters and the selection of the parameters themselves. The choice of model parameters was determined by the Bayesian model’s predictions as a function of the data and the data set size, hence it was necessary to choose a new parameter to calculate the expected distribution of the parameters over the data set size. We show that the proposed method can be used in many other computer vision tasks, such as object categorization, video summarization, image classification, and learning from low dimensional data, and it is applicable to these applications.


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