Including a Belief Function in a Deep Generative Feature Learning Network


Including a Belief Function in a Deep Generative Feature Learning Network – Deep learning has recently shown considerable success in various fields of human-computer interaction. However, the most important problem that we face in deep learning is learning a human brain. To solve this problem, we propose using Convolutional Neural Networks (CNNs) for feature extraction and learning of multiple entities. In addition, we propose an end-to-end learning method for training CNNs, which can be seen as a method of self-organising the output. In this paper, we propose a different learning method combining a CNN for feature extraction and CNN for learning to model multiple entities, together with a Deep Learning Network for the output.

We investigate supervised deep learning for visual tracking. We propose a technique that extracts a representation of the sensor-dependent motion of the object and a neural network that uses a convolutional neural network to predict the appearance and orientation of the object accordingly. This representation can be used by using a convolutional neural network based on object-view-label pairs. We design and test a deep tracking system to accurately track a pair of objects. Through experimental evaluation, we demonstrate the effectiveness of our approach and demonstrate the effectiveness of our system on various real-world datasets.

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Including a Belief Function in a Deep Generative Feature Learning Network

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  • Efficient Sublinear Learning for Latent Variable Models

    Super-Dense: Robust Deep Convolutional Neural Network Embedding via Self-Adaptive RegularizationWe investigate supervised deep learning for visual tracking. We propose a technique that extracts a representation of the sensor-dependent motion of the object and a neural network that uses a convolutional neural network to predict the appearance and orientation of the object accordingly. This representation can be used by using a convolutional neural network based on object-view-label pairs. We design and test a deep tracking system to accurately track a pair of objects. Through experimental evaluation, we demonstrate the effectiveness of our approach and demonstrate the effectiveness of our system on various real-world datasets.


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