Deep Learning Based on Time Shift Dynamics for Video Prediction


Deep Learning Based on Time Shift Dynamics for Video Prediction – The video classification problem was proposed in the context of automatic speech recognition (ASR). At first, it was shown that supervised learning can be used to classify videos that are of similar nature to the ones in ASR dataset. The key assumption is that the similarity of the videos is maximized by a simple, yet highly expressive, method. This model was then exploited to create a model for human action recognition. The results show that as the similarity of the videos increases, the discriminative power of this model is increased. The proposed model was used for the task of video classification. As a result of this model, the learned action recognition performance increased almost from 50% for English-English and 20% for German-English, without any drop in recognition accuracy.

The proposed model-based learning algorithm, Stochastic Gradient Descent (SGD), is a recurrent-learning neural network method for supervised learning of multiple sequential states. In this paper, SGD achieves state-of-the-art performance when used in conjunction with supervised learning, in terms of training samples, and the prediction accuracy of the underlying models. Experimental results suggest that SGD significantly outperforms the state-of-the-art on a test set in sequential classification task, comparing with other state-of-the-art models in many sequential tasks (e.g., unsupervised classification).

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Deep Learning Based on Time Shift Dynamics for Video Prediction

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    Learning Spatial Relations in the Past with Recurrent Neural NetworksThe proposed model-based learning algorithm, Stochastic Gradient Descent (SGD), is a recurrent-learning neural network method for supervised learning of multiple sequential states. In this paper, SGD achieves state-of-the-art performance when used in conjunction with supervised learning, in terms of training samples, and the prediction accuracy of the underlying models. Experimental results suggest that SGD significantly outperforms the state-of-the-art on a test set in sequential classification task, comparing with other state-of-the-art models in many sequential tasks (e.g., unsupervised classification).


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