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Understanding Center Loss Based Network for Image Retrieval with Few Training Data

  • Pallabi GhoshEmail author
  • Larry S. Davis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)

Abstract

Performance of convolutional neural network based image retrieval depends on the characteristics and statistics of the data being used for training. We show that for training datasets with a large number of classes but small number of images per class, the combination of cross-entropy loss and center loss works better than either of the losses alone. While cross-entropy loss tries to minimize misclassification of data, center loss minimizes the embedding space distance of each point in a class to its center, bringing together data-points belonging to the same class.

Keywords

Center loss Image retrieval Small training dataset 

Notes

Acknowledgement

This work was supported by the DARPA MediFor program under cooperative agreement FA87501620191, “Physical and Semantic Integrity Measures for Media Forensics”. The authors acknowledge the Maryland Advanced Research Computing Center (MARCC) for providing computing resources.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of MarylandCollege ParkUSA

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