Original paper
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
Abstract
Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural network using only unlabeled data. We train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. We find that this simple...
Paper Details
Title
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
Published Date
Dec 8, 2014
Journal
Volume
27
Pages
766 - 774
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