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A survey on face data augmentation for the training of deep neural networks
- Source :
- Neural Computing and Applications. 32:15503-15531
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- The quality and size of training set have a 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 review the existing works of face data augmentation from the perspectives of the transformation types and methods, with the state-of-the-art approaches involved. Among all these approaches, we put the emphasis on the deep learning-based works, especially the generative adversarial networks which have been recognized as more powerful and effective tools in recent years. We present their principles, discuss the results and show their applications as well as limitations. Different evaluation metrics for evaluating these approaches are also introduced. We point out the challenges and opportunities in the field of face data augmentation and provide brief yet insightful discussions.
- Subjects :
- 0209 industrial biotechnology
Training set
Point (typography)
Computer science
business.industry
media_common.quotation_subject
Deep learning
02 engineering and technology
Data science
Training (civil)
Field (computer science)
020901 industrial engineering & automation
Artificial Intelligence
Face (geometry)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Quality (business)
Artificial intelligence
business
Software
media_common
Subjects
Details
- ISSN :
- 14333058 and 09410643
- Volume :
- 32
- Database :
- OpenAIRE
- Journal :
- Neural Computing and Applications
- Accession number :
- edsair.doi...........e60312625808fb5bf564d62b04cd4205