Deep neural network training with hidden panels for nonlinear adaptive filtering


Deep neural network training with hidden panels for nonlinear adaptive filtering – We present a novel network-model-guided approach to learning to-watch video data. Through a deep learning method that learns an encoding function for each frame of the video sequence, the network is trained with an eye-tracking strategy on the sequence, which is then used to predict future frames of the relevant sequence. Our model uses a multi-sensor convolutional neural network that can learn the visual attribute of the input video. We propose a novel framework, called ConvNet-CNN, to learn the visual attribute of the input video from multi-view regression. We show that our method outperforms three state-of-the-art CNN architectures on various datasets.

In this paper, we propose a new fully convolutional neural network (FCNN) to tackle the 3D object recognition problem. We propose Convolutional Neural Network (CNN) for grasping 3D objects from videos. The CNN is trained end to end, with the aim of learning object detection and trajectory based object classification, without using any hand-crafted convolutional features. Compared to existing CNN models with a very small number of parameters, our CNN has a few parameters which are more discriminative to improve object detection. We show that our CNN is not only able to reliably classify high quality object instances without any hand-crafted object features. This is important because CNN can be used for improving object category accuracy if the 2D object recognition process is used. In addition to CNN, our CNN is also able to accurately classify objects which are very dense objects. Our CNN is implemented using an interactive 3D object prediction platform which demonstrates our accuracy on the challenging task of 2D objects classification on a 3D MNIST dataset.

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Deep neural network training with hidden panels for nonlinear adaptive filtering

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  • Proceedings of the 38th Annual Workshop of the Austrian Machine Learning Association (ÖDAI), 2013

    Deep Learning with a Unified Deep Convolutional Network for Video ClassificationIn this paper, we propose a new fully convolutional neural network (FCNN) to tackle the 3D object recognition problem. We propose Convolutional Neural Network (CNN) for grasping 3D objects from videos. The CNN is trained end to end, with the aim of learning object detection and trajectory based object classification, without using any hand-crafted convolutional features. Compared to existing CNN models with a very small number of parameters, our CNN has a few parameters which are more discriminative to improve object detection. We show that our CNN is not only able to reliably classify high quality object instances without any hand-crafted object features. This is important because CNN can be used for improving object category accuracy if the 2D object recognition process is used. In addition to CNN, our CNN is also able to accurately classify objects which are very dense objects. Our CNN is implemented using an interactive 3D object prediction platform which demonstrates our accuracy on the challenging task of 2D objects classification on a 3D MNIST dataset.


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