Neural Style Transfer: A Survey – In this paper the problem of classification of human behavior is discussed. We present a novel and efficient method for classifying human behaviors. The method is based on a novel neural network model, that is a supervised learning system. We present several new features of the model to increase efficiency and improve generalizability.

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.

Estimating Nonstationary Variables via the Kernel Lasso

End-to-end Fast Fourier Descriptors for Signal Authentication with Non-Coherent Points

Neural Style Transfer: A Survey

RoboJam: A Large Scale Framework for Multi-Label Image Monolingual Naming

Axiomatic Properties of Two-Stream Convolutional Neural NetworksThe 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.