Training of Deep Convolutional Neural Networks for Large-Scale Video Classification


Training of Deep Convolutional Neural Networks for Large-Scale Video Classification – While the majority of the methods used for video classification make use of linear features derived from the target sequence, many existing models use a series of feature vectors instead of image features. We propose a novel class of features which is a mixture of linear and nonconvex representations of image labels that is significantly richer in information and is more appropriate for classifying a class of images. The new feature representation can be generalized to any nonlinear or non-convex matrix or is trained as a linear model using the class of image labels as training data. We illustrate how the new representation is used for learning and learning-based classification using both synthetic and real neural networks.

In this paper, a new deep convolutional neural network architecture was proposed, that has the ability to efficiently solve a number of problems. The architecture, that is, a neural network for a hierarchical multi-dimensional manifold space, can cope with multiple multilinear models in a supervised learning scenario. Such a structure, a neural network can be trained and used to perform the supervised learning tasks. The learning process is made more efficient by incorporating the information gained from the multilinear representations.

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Training of Deep Convolutional Neural Networks for Large-Scale Video Classification

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    Cascaded Multi-level Feature Space for Graph EmbeddingIn this paper, a new deep convolutional neural network architecture was proposed, that has the ability to efficiently solve a number of problems. The architecture, that is, a neural network for a hierarchical multi-dimensional manifold space, can cope with multiple multilinear models in a supervised learning scenario. Such a structure, a neural network can be trained and used to perform the supervised learning tasks. The learning process is made more efficient by incorporating the information gained from the multilinear representations.


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