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Unsupervised Learning of Discriminative Attributes and Visual Representations
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Attributes offer useful mid-level features to interpret visual data. While most attribute learning methods are supervised by costly human-generated labels, we introduce a simple yet powerful unsupervised approach to learn and predict visual attributes directly from data. Given a large unlabeled image collection as input, we train deep Convolutional Neural Networks (CNNs) to output a set of discriminative, binary attributes often with semantic meanings. Specifically, we first train a CNN coupled
doi:10.1109/cvpr.2016.559
dblp:conf/cvpr/HuangLT16
fatcat:zt4qarlwobbslb52b5ylylelvu