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Semi-Supervised Feature Learning for Off-Line Writer Identifications
Conventional approaches used supervised learning to estimate off-line wr...
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Sparse Coding on Stereo Video for Object Detection
Deep Convolutional Neural Networks (DCNN) require millions of labeled tr...
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Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs
Cryo-electron microscopy (cryoEM) is fast becoming the preferred method ...
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Learning a Fully Convolutional Network for Object Recognition using very few Data
In recent years, data-driven methods have shown great success for extrac...
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Unsupervised feature learning by augmenting single images
When deep learning is applied to visual object recognition, data augment...
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Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Deep convolutional networks have proven to be very successful in learnin...
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Clustering Based Feature Learning on Variable Stars
The success of automatic classification of variable stars strongly depen...
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Selective Unsupervised Feature Learning with Convolutional Neural Network (S-CNN)
Supervised learning of convolutional neural networks (CNNs) can require very large amounts of labeled data. Labeling thousands or millions of training examples can be extremely time consuming and costly. One direction towards addressing this problem is to create features from unlabeled data. In this paper we propose a new method for training a CNN, with no need for labeled instances. This method for unsupervised feature learning is then successfully applied to a challenging object recognition task. The proposed algorithm is relatively simple, but attains accuracy comparable to that of more sophisticated methods. The proposed method is significantly easier to train, compared to existing CNN methods, making fewer requirements on manually labeled training data. It is also shown to be resistant to overfitting. We provide results on some well-known datasets, namely STL-10, CIFAR-10, and CIFAR-100. The results show that our method provides competitive performance compared with existing alternative methods. Selective Convolutional Neural Network (S-CNN) is a simple and fast algorithm, it introduces a new way to do unsupervised feature learning, and it provides discriminative features which generalize well.
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