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.

Recent research on learning and classification problems has demonstrated that models trained on human emotion may not be capable of solving the classification task. Yet in general, models trained on emotion models are more accurate than human-trained ones. For instance, trained models can generate more accurate classification results for emotion recognition tasks than human-trained ones. Recent results on learning and classification tasks also demonstrate that human emotion is more useful in learning models trained on emotion models. However, human-trained human-classifiers are much more difficult to interpret. For instance, human emotion detection models are able to generate more accurate classification results for emotion recognition tasks than human-trained ones. This paper presents an open challenge for machine learning and classification researchers in the area of learning and classification tasks.

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Generative Autoencoders for Active Learning

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  • Visual Tracking via Deep Generative Models

    Learning Discriminative Deep Convolutional Neural Networks for Multi-Stage Mobile ApplicationsRecent research on learning and classification problems has demonstrated that models trained on human emotion may not be capable of solving the classification task. Yet in general, models trained on emotion models are more accurate than human-trained ones. For instance, trained models can generate more accurate classification results for emotion recognition tasks than human-trained ones. Recent results on learning and classification tasks also demonstrate that human emotion is more useful in learning models trained on emotion models. However, human-trained human-classifiers are much more difficult to interpret. For instance, human emotion detection models are able to generate more accurate classification results for emotion recognition tasks than human-trained ones. This paper presents an open challenge for machine learning and classification researchers in the area of learning and classification tasks.


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