An Empirical Evaluation of Unsupervised Learning Methods based on Hidden Markov Models

An Empirical Evaluation of Unsupervised Learning Methods based on Hidden Markov Models – The use of large datasets for data augmentation is a common and valuable tool for building scalable algorithms. In this paper we provide a new perspective on data augmentation and apply it to a novel dimension of data that is common to most computer vision applications. We describe two methods of learning the dimension of data augmentation using the multi-dimensional tensor norm and the multinomial regularizer, respectively, of a dataset of tensor-regularized linear functions. We define the dimension of data augmentation and how it affects the performance of the multinomial regularizer, the tensor norm, and the tensor regularizer. We use the dimension of data augmentation to demonstrate that the multinomial regularizer learns to outperform the tensor norm, and the multinomial regularizer is the best discriminative discriminative regularizer.

We propose a novel framework for automatic video quality control, which includes an active learning setting, which can be used to learn the properties of the video by predicting the video quality parameters for each individual instance. This framework is built upon a novel training scenario where the training data is generated by an agent and the control system learns to optimize the video quality parameters, using neural networks. We demonstrate that our framework leads to effective learning of video to improve the quality of the video. We discuss various methods and show how our framework can be used as a generic framework for video quality control and an efficient user-friendly software.

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An Empirical Evaluation of Unsupervised Learning Methods based on Hidden Markov Models

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  • Stereoscopic Video Object Parsing by Multi-modal Transfer Learning

    A Unified Approach for Online Video Quality Control using Deep Neural Network TechniqueWe propose a novel framework for automatic video quality control, which includes an active learning setting, which can be used to learn the properties of the video by predicting the video quality parameters for each individual instance. This framework is built upon a novel training scenario where the training data is generated by an agent and the control system learns to optimize the video quality parameters, using neural networks. We demonstrate that our framework leads to effective learning of video to improve the quality of the video. We discuss various methods and show how our framework can be used as a generic framework for video quality control and an efficient user-friendly software.


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