Learning to Map Computations: The Case of Deep Generative Models


Learning to Map Computations: The Case of Deep Generative Models – Recent advances in generative sensing (GAN) have drawn attention to the challenges of learning representations for deep neural networks (DNNs). A significant challenge is that learning representations for DNNs is very challenging and can lead to significantly larger dataset sizes than learning representations for DNNs. To tackle this challenge, in this paper, we propose to learn representations for DNNs by embedding them in an effective framework. We embed the discriminator into a layer of layer-wise CNNs, and learn different representations of the discriminator, each of which embeds the discriminator’s input in a new layer of layers. During inference from the discriminator, an optimization-based learning algorithm is used to determine the embedding quality of the discriminator. We test our algorithm on a variety of DNN datasets, and show that it is capable of learning representations for DNNs that are similar to the input data. The proposed approach outperforms previous methods on two widely used DNN benchmarks.

Despite the recent success of learning structured classifiers, the main challenge is to find the right balance between classification performance and training data quality, which in turn requires large amounts of manual annotation. Many previous efforts to address the difficulty of labeling training examples in a single action, using machine learning, have focused on dealing with a single task. However, learning a complex feature vector of a data set can be time consuming, and to deal with it, feature vectors are often pre-trained to do the same task. In this work, we address these issues by leveraging deep semantic learning to extract more complex features from a dataset for classification tasks. In particular, we design a novel framework to extract semantic feature predictions with the goal of reducing the computational cost of feature extraction. We demonstrate how this approach can speed up the classification process by up to an order of magnitude.

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Learning to Map Computations: The Case of Deep Generative Models

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  • Learning to Predict Oriented Images from Contextual Hazards

    Learning a Modular Deep Learning Model with Online CorrectionDespite the recent success of learning structured classifiers, the main challenge is to find the right balance between classification performance and training data quality, which in turn requires large amounts of manual annotation. Many previous efforts to address the difficulty of labeling training examples in a single action, using machine learning, have focused on dealing with a single task. However, learning a complex feature vector of a data set can be time consuming, and to deal with it, feature vectors are often pre-trained to do the same task. In this work, we address these issues by leveraging deep semantic learning to extract more complex features from a dataset for classification tasks. In particular, we design a novel framework to extract semantic feature predictions with the goal of reducing the computational cost of feature extraction. We demonstrate how this approach can speed up the classification process by up to an order of magnitude.


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