Learning image similarity metrics in an end-to-end fashion with deep networks has demonstrated excellent results on tasks such as clustering and retrieval. However, current methods, all focus on a very local view of the data. In this paper, we propose a new metric learning scheme, based on structured prediction, that is aware of the global structure of the embedding space, and which is designed to optimize a clustering quality metric (NMI). We show state of the art performance on standard datasets, such as CUB200-2011 [37], Cars196 [18] , and Stanford online products [30] on NMI and R@K evaluation metrics.
Later in REF , another structured prediction-based method is designed to directly optimize the deep neural network with a clustering quality metric.
Deep Metric Learning via Facility Location
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science