2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVASR-5.8
Paper Title Learning Imbalanced Datasets with Maximum Margin Loss
Authors Haeyong Kang, Thang Vu, Chang D. Yoo, Korea Advanced Institute of Science and Technology, Republic of Korea
SessionMLR-APPL-IVASR-5: Machine learning for image and video analysis, synthesis, and retrieval 5
LocationArea C
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the deep model tends to predict the majority classes rather than the minority ones. For better generalization on the minority classes, the proposed Maximum Margin (MM) loss function is newly designed by minimizing a margin-based generalization bound through the shifting decision bound. As a prior study, the theoretically principled label-distribution-aware margin (LDAM) loss had been successfully applied with classical strategies such as re-weighting or re-sampling. However, the maximum margin loss function has not been investigated so far. In this study, we evaluate the two types of hard maximum margin-based decision boundary shift with training schedule on artificially imbalanced CIFAR-10/100 and show the effectiveness.