Cavity Filling: Pseudo-Feature Generation for Multi-Class Imbalanced Data Problems in Deep Learning

Tomohiko Konno; Michiaki Iwazume

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arXiv · 1807.06538

Cavity Filling: Pseudo-Feature Generation for Multi-Class Imbalanced Data Problems in Deep Learning

Tomohiko Konno·Michiaki Iwazume

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Abstract

Herein, we generate pseudo-features based on the multivariate probability distributions obtained from the feature maps in layers of trained deep neural networks. Further, we augment the minor-class data based on these generated pseudo-features to overcome the imbalanced data problems. The proposed method, i.e., cavity filling, improves the deep learning capabilities in several problems because all the real-world data are observed to be imbalanced.

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cs.LG cs.AI cs.CL cs.CV stat.ML

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Tomohiko Konno, Michiaki Iwazume. 2018-07-17. Cavity Filling: Pseudo-Feature Generation for Multi-Class Imbalanced Data Problems in Deep Learning. https://arxiv.org/abs/1807.06538

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