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Miguel A. Bautista

2 accepted papers

2017

Deep Unsupervised Similarity Learning Using Partially Ordered Sets

CVPR 2017poster

Unsupervised learning of visual similarities is of paramount importance to computer vision, particularly due to lacking training data for fine-grained similarities. Deep learning of similarities is often based on relationships between pairs or triplets of samples. Many of these relations are unrelia…

Cited by 34PDFcodeScholar
2016

CliqueCNN: Deep Unsupervised Exemplar Learning

NeurIPS 2016poster

Exemplar learning is a powerful paradigm for discovering visual similarities in an unsupervised manner. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many nega…