CliqueCNN: Deep Unsupervised Exemplar Learning
This addresses the challenge of unsupervised visual similarity learning for computer vision applications, but it is incremental as it builds on existing deep learning paradigms.
The paper tackled the problem of training deep convolutional neural networks for unsupervised exemplar learning, where single positive samples and unreliable relationships impair learning, by proposing a method to group similar samples into cliques and frame learning as clique categorization tasks, resulting in competitive performance on posture analysis and object classification.
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.