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On learning density aware embeddings
Published in IEEE Computer Society
2019
Volume: 2019-June
   
Pages: 4879 - 4887
Abstract
Deep metric learning algorithms have been utilized to learn discriminative and generalizable models which are effective for classifying unseen classes. In this paper, a novel noise tolerant deep metric learning algorithm is proposed. The proposed method, termed as Density Aware Metric Learning, enforces the model to learn embeddings that are pulled towards the most dense region of the clusters for each class. It is achieved by iteratively shifting the estimate of the center towards the dense region of the cluster thereby leading to faster convergence and higher generalizability. In addition to this, the approach is robust to noisy samples in the training data, often present as outliers. Detailed experiments and analysis on two challenging cross-modal face recognition databases and two popular object recognition databases exhibit the efficacy of the proposed approach. It has superior convergence, requires lesser training time, and yields better accuracies than several popular deep metric learning methods. © 2019 IEEE.
About the journal
JournalData powered by TypesetProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherData powered by TypesetIEEE Computer Society
ISSN10636919