Adding Attention to Subspace Metric Learning

Adding Attention to Subspace Metric Learning

Name1, Author

Proceedings of Machine Learning Research-Under Review 2020

Abstract : Deep metric learning is a compelling approach to learn an embedding space where the images from the same class are encouraged to be close and images from different classes are pushed away. Current deep metric learning approaches are inadequate to explain visually which regions contribute to the learning embedding space. Visual explanations of images are particularly of interest in medical imaging, since interpretation directly impacts the diagnosis, treatment planning and follow-up of many diseases. In this work, we propose a novel attention-based metric learning approach for medical images and seek to bridge the gap between visual interpretability and deep metric learning. Our method builds upon a divide-and-conquer strategy, where multiple learners refine subspaces of a global embedding. Furthermore, we integrated an attention module that provides visual insights of discriminative regions that contribute to the clustering of image sets and to the visual-ization of their embedding features. We evaluate the benefits of using an attention-based approach for deep metric learning in the tasks of image clustering and image retrieval using a public benchmark on skin lesion detection. Our attentive deep metric learning improves the performance over recent state-of-the-art, while also providing visual interpretability of image similarities.