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.

We use Recurrent Neural Network (RNN) as an end-to-end learning framework for classification. We present a novel method for learning recurrent neural networks for classification of patients with cancer in the first part of the article. We first propose a generic and effective method for the learning, and use it to build a recurrent neural network model. The proposed algorithm is shown to learn to classify patients with cancer from a corpus of more than 600,000 CT brain images.

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Generative Autoencoders for Active Learning

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    Deep Learning for Detection of Cervical Cancer by Cervical Disease Classification with Recurrent Neural NetworksWe use Recurrent Neural Network (RNN) as an end-to-end learning framework for classification. We present a novel method for learning recurrent neural networks for classification of patients with cancer in the first part of the article. We first propose a generic and effective method for the learning, and use it to build a recurrent neural network model. The proposed algorithm is shown to learn to classify patients with cancer from a corpus of more than 600,000 CT brain images.


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