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A Survey on Face Data Augmentation
[article]
2019
arXiv
pre-print
The quality and size of training set have great impact on the results of deep learning-based face related tasks. However, collecting and labeling adequate samples with high quality and balanced distributions still remains a laborious and expensive work, and various data augmentation techniques have thus been widely used to enrich the training dataset. In this paper, we systematically review the existing works of face data augmentation from the perspectives of the transformation types and
arXiv:1904.11685v1
fatcat:phcwwc7gcfablgytt6itr6xade