DeepKSPD: Learning to detect unusual motion patterns in videos


DeepKSPD: Learning to detect unusual motion patterns in videos – In a video game, the players have chosen various actions which could be useful in achieving their goals or in avoiding mistakes. Using a real-world video simulation as an example, we show that the player’s actions in games are not relevant for a certain goal: a robotic walking. We propose a novel method to learn the player’s action from the video frames and generate a novel motion vector representing the player’s state. Our method is flexible enough to model the player’s actions but requires the video to be captured by an entirely different camera, or only a set of clips. The proposed method is a novel method of learning a player’s action in a video game, by analyzing the camera sequences of the game and by leveraging the information obtained from the video frames captured by the player’s actions and from the video frames captured from different video frames.

Semi-supervised learning systems employ the nonlinearity of the inputs to train the network to make more observations per second. However, it is generally not known what is the optimal value of these representations as a function of the training set. We propose a non-linear learning rule to estimate the true values of the hidden representations, and show that this strategy, called learning the value of the noise by the nonlinearity, is accurate enough to achieve good results.

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DeepKSPD: Learning to detect unusual motion patterns in videos

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  • An evaluation of the training of deep neural networks for hypercortical segmentation of electroencephalograms in brain studies

    Tuning for Semi-Supervised Learning via Clustering and Sparse LiftingSemi-supervised learning systems employ the nonlinearity of the inputs to train the network to make more observations per second. However, it is generally not known what is the optimal value of these representations as a function of the training set. We propose a non-linear learning rule to estimate the true values of the hidden representations, and show that this strategy, called learning the value of the noise by the nonlinearity, is accurate enough to achieve good results.


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