Selective unsupervised feature learning with Convolutional Neural Network (S-CNN)

  title={Selective unsupervised feature learning with Convolutional Neural Network (S-CNN)},
  author={Amir Ghaderi and V. Athitsos},
  journal={2016 23rd International Conference on Pattern Recognition (ICPR)},
  • Amir Ghaderi, V. Athitsos
  • Published 2016
  • Computer Science
  • 2016 23rd International Conference on Pattern Recognition (ICPR)
  • 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… CONTINUE READING
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