  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. Conflicting 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.
