Generative Autoencoders for Active Learning


Generative Autoencoders for Active Learning – Motivated by the challenges associated with supervised learning and computational vision, we propose to use a neural network trained to predict from images a hidden representation of the full image, in addition to the visual data. The model trained with the full image is fed with a convolutional neural network trained to predict all the features that the model can predict in the full image. Extensive experiments show that our proposed model can detect visual features from an image and that it is able to predict whether the image is visual or not. We further show that training the model with this representation of the full image can result in significant improvements.

As a major challenge in machine learning, a significant number of machine learning tasks use a low-dimensional representation of the data. It is hard to directly optimize the representation by training the deep network on a high-dimensional representation. In this paper, we propose a novel non-linear learning algorithm for model-based decision support for deep networks, wherein the high-dimensional representations of the data are optimized using a weighted least-squares loss to the loss function as well as a non-linear learning objective. Our algorithm is based on a simple yet effective regularization term which is efficient and practical, but requires no supervision for the deep network. The algorithm is applied to support the decision support task in which the input data of both data types for various decision contexts is shared (e.g. from the medical record, to users of healthcare services). In case of data sharing, it is also possible to compute weighted least-squares loss functions such that the data of different types are not shared by all models for a set of multiple decision contexts. We demonstrate the effectiveness of the proposed algorithm in two real and real-world scenarios.

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Generative Autoencoders for Active Learning

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  • View-Hosting: Streaming views on a single screen

    Feature Representation for Deep Neural Network Compression: Application to Compressive Sensing of MammogramsAs a major challenge in machine learning, a significant number of machine learning tasks use a low-dimensional representation of the data. It is hard to directly optimize the representation by training the deep network on a high-dimensional representation. In this paper, we propose a novel non-linear learning algorithm for model-based decision support for deep networks, wherein the high-dimensional representations of the data are optimized using a weighted least-squares loss to the loss function as well as a non-linear learning objective. Our algorithm is based on a simple yet effective regularization term which is efficient and practical, but requires no supervision for the deep network. The algorithm is applied to support the decision support task in which the input data of both data types for various decision contexts is shared (e.g. from the medical record, to users of healthcare services). In case of data sharing, it is also possible to compute weighted least-squares loss functions such that the data of different types are not shared by all models for a set of multiple decision contexts. We demonstrate the effectiveness of the proposed algorithm in two real and real-world scenarios.


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