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 [36], Cars196 [18], and Stanford online products [29] on NMI and R@K evaluation metrics.
More recently, Song et al. REF proposed a new metric learning framework which encourages the network to learn an embedding function that directly optimizes a clustering quality metric.
Learnable Structured Clustering Framework for Deep Metric Learning
ArXiv
ArXiv
Mathematics Computer Science