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Unsupervised learning of visual similarities is of paramount importance tocomputer vision, particularly due to lacking training data for fine-grainedsimilarities. Deep learning of similarities is often based on relationshipsbetween pairs or triplets of samples. Many of these relations are unreliableand mutually contradicting, implying inconsistencies when trained withoutsupervision information that relates different tuples or triplets to eachother. To overcome this problem, we use local estimates of reliable(dis-)similarities to initially group samples into compact surrogate classesand use local partial orders of samples to classes to link classes to eachother. Similarity learning is then formulated as a partial ordering task withsoft correspondences of all samples to classes. Adopting a strategy ofself-supervision, a CNN is trained to optimally represent samples in a mutuallyconsistent manner while updating the classes. The similarity learning andgrouping procedure are integrated in a single model and optimized jointly. Theproposed unsupervised approach shows competitive performance on detailed poseestimation and object classification.

Deep Unsupervised Similarity Learning using Partially Ordered Sets