Axiomatic Properties of Two-Stream Convolutional Neural Networks


Axiomatic Properties of Two-Stream Convolutional Neural Networks – 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.

Video has been used to create the illusion of being human-like to a large extent, yet this may not be able to provide a good model of the human personality. Recently a new approach to model human intelligence (HIT) called the Self-Organizing System (SA) has been proposed to understand the self-organizing power of video. Here, we propose a new model that has a direct representation of the human personality, and its ability to generate videos through a learned network of attention mechanisms that are a key to its intelligence. The proposed model has the ability to automatically learn a new video model from its previous learning process, and adapt to its new video data. Experimental results on a variety of real-world videos show that the proposed model generates the same and more human-like video than previous models.

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Axiomatic Properties of Two-Stream Convolutional Neural Networks

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    Fully Parallel Supervised LAD-SLAM for Energy-Efficient Applications in Video ProcessingVideo has been used to create the illusion of being human-like to a large extent, yet this may not be able to provide a good model of the human personality. Recently a new approach to model human intelligence (HIT) called the Self-Organizing System (SA) has been proposed to understand the self-organizing power of video. Here, we propose a new model that has a direct representation of the human personality, and its ability to generate videos through a learned network of attention mechanisms that are a key to its intelligence. The proposed model has the ability to automatically learn a new video model from its previous learning process, and adapt to its new video data. Experimental results on a variety of real-world videos show that the proposed model generates the same and more human-like video than previous models.


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