A Bayesian Approach to Learning Deep Feature Representations – Generative adversarial networks (GANs) have been successfully used for adversarial tracking in security applications. In this work, we propose a novel architecture for deep adversarial tracking. The architecture consists of two stages: (1) a stochastic adversarial network, which is conditioned on a data matrix containing the training samples, and (2) a fully-connected adversarial network, which is modeled as a convex matrix and is trained by a loss function. We show that the proposed scheme achieves the best performance with respect to all prior approaches.

We propose an efficient approach to predict the next action of an action-sequence with a deep learning approach. The prediction is performed directly from an online prediction of the action-sequence from a prediction model, thus offering an efficient method for learning action-sequence based action-sequence models for learning a deep recurrent network. Our algorithm performs both a classification and a regression supervised problem to learn a supervised action-sequence based action-sequence model, which are then used to predict the next action of the action in the sequence. We show that the predictive ability of the proposed method is based on a simple prediction of the action of the action sequence to the prediction model. Our method is also suitable for both supervised and regression supervised action-sequence models.

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# A Bayesian Approach to Learning Deep Feature Representations

Recurrent Reinforcement Learning with Spatially-Varying Recurrent Neural NetworksWe propose an efficient approach to predict the next action of an action-sequence with a deep learning approach. The prediction is performed directly from an online prediction of the action-sequence from a prediction model, thus offering an efficient method for learning action-sequence based action-sequence models for learning a deep recurrent network. Our algorithm performs both a classification and a regression supervised problem to learn a supervised action-sequence based action-sequence model, which are then used to predict the next action of the action in the sequence. We show that the predictive ability of the proposed method is based on a simple prediction of the action of the action sequence to the prediction model. Our method is also suitable for both supervised and regression supervised action-sequence models.