Learning a Human-Level Auditory Processing Unit


Learning a Human-Level Auditory Processing Unit – We propose a deep generative model that can learn to produce a variety of emotions. Our model includes an external representation of the emotions of a given scene in which the emotions of human beings are encoded. To learn a complex emotion representation for the scenes, we combine human-level language and external representations of the emotions from the world as a generative model of the scene. By leveraging our generative model, we generate visual summaries of the emotion of human beings that we can then use to make predictions about the emotions of the human and the environment. The models use these summaries for the task of estimating human emotion recognition.

The present work investigates the problem of learning Deep Generative models with log-like motion features for recognition task. We consider the problem of learning Generative representations that take as input the motion feature vectors of a dataset, a video and a text. In the video representation space, we adopt the state-of-the-art for CNN classification, which is a non-linear embedding of the video into a sparse set of convolutional embeddings. The resulting models perform well regardless of feature-based classification, and can perform very well on large datasets with a fixed input. In addition, the proposed models can learn to generate a variety of motion features for different types of recognition tasks, making them suitable for use as training data.

Evaluation of Deep Learning Methods for Accurate Vehicle Speed Matching

Good, Better, Strong, and Always True

Learning a Human-Level Auditory Processing Unit

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  • Object Tracking in the Wild: A Benchmark for Feature Extraction

    Learning Deep Generative Models with Log-Like Motion FeaturesThe present work investigates the problem of learning Deep Generative models with log-like motion features for recognition task. We consider the problem of learning Generative representations that take as input the motion feature vectors of a dataset, a video and a text. In the video representation space, we adopt the state-of-the-art for CNN classification, which is a non-linear embedding of the video into a sparse set of convolutional embeddings. The resulting models perform well regardless of feature-based classification, and can perform very well on large datasets with a fixed input. In addition, the proposed models can learn to generate a variety of motion features for different types of recognition tasks, making them suitable for use as training data.


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