Data Augmentation Method by Applying Color Perturbation of Inverse PSNR and Geometric Transformations for Object Recognition Based on Deep Learning
Deep learning is applied in various manufacturing domains. To train a deep learning network, we must collect a sufficient amount of training data. However, it is difficult to collect image datasets required to train the networks to perform object recognition, especially because target items that are...
Main Authors: | Eun Kyeong Kim, Hansoo Lee, Jin Yong Kim, Sungshin Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-05-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/10/11/3755 |
Similar Items
-
Adaptive Data Augmentation to Achieve Noise Robustness and Overcome Data Deficiency for Deep Learning
by: Eunkyeong Kim, et al.
Published: (2021-06-01) -
Wasserstein Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis
by: Hansoo Lee, et al.
Published: (2020-02-01) -
An Analysis of the Effect of Data Augmentation Methods: Experiments for a Musical Genre Classification Task
by: Rémi Mignot, et al.
Published: (2019-12-01) -
A Data Augmentation Scheme for Geometric Deep Learning in Personalized Brain–Computer Interfaces
by: Fotis P. Kalaganis, et al.
Published: (2020-01-01) -
Distribution-preserving data augmentation
by: Nurdan Ayse Saran, et al.
Published: (2021-05-01)