Mining On Manifolds: Metric Learning Without Labels
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum . 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018 – 4 citations
[Paper]
In this work we present a novel unsupervised framework for hard training example mining. The only input to the method is a collection of images relevant to the target application and a meaningful initial representation, provided e.g. by pre-trained CNN. Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds. Both types of examples are revealed by disagreements between Euclidean and manifold similarities. The discovered examples can be used in training with any discriminative loss. The method is applied to unsupervised fine-tuning of pre-trained networks for fine-grained classification and particular object retrieval. Our models are on par or are outperforming prior models that are fully or partially supervised.