@conference {arXiv:1608.08792,
	title = {CliqueCNN: Deep Unsupervised Exemplar Learning},
	booktitle = {Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS)},
	year = {2016},
	publisher = {MIT Press},
	organization = {MIT Press},
	address = {Barcelona},
	abstract = {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 negatives, and unreliable
relationships between most samples, training of Convolutional Neural networks is
impaired. Given weak estimates of local distance we propose a single optimization
problem to extract batches of samples with mutually consistent relations. Conflict-
ing relations are distributed over different batches and similar samples are grouped
into compact cliques. Learning exemplar similarities is framed as a sequence of
clique categorization tasks. The CNN then consolidates transitivity relations within
and between cliques and learns a single representation for all samples without
the need for labels. The proposed unsupervised approach has shown competitive
performance on detailed posture analysis and object classification.},
	url = {https://arxiv.org/abs/1608.08792},
	author = {Miguel Bautista and Sanakoyeu, A. and Sutter, E. and Bj{\"o}rn Ommer}
}
