- Computer Science
- Published in Journal of Big Data 2019
DOI:10.1186/s40537-019-0197-0
A survey on Image Data Augmentation for Deep Learning
@article{Shorten2019ASO,
title={A survey on Image Data Augmentation for Deep Learning},
author={Connor Shorten and Taghi M. Khoshgoftaar},
journal={Journal of Big Data},
year={2019},
volume={6},
pages={1-48}
}Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem… CONTINUE READING
Figures, Tables, and Topics from this paper.
Figures and Tables
figure 1
table 1
figure 2
table 2
figure 3
table 3
figure 4
table 4
figure 5
table 5
figure 6
table 6
figure 7
table 7
figure 8
table 8
figure 9
table 9
figure 10
table 10
figure 11
figure 12
figure 13
figure 14
figure 15
figure 16
figure 17
figure 18
figure 19
figure 20
figure 21
figure 22
figure 23
figure 24
figure 25
figure 26
figure 27
figure 28
figure 29
figure 31
figure 32
figure 33
Citations
Publications citing this paper.
Synthetic Data for Deep Learning
VIEW 1 EXCERPT
CITES METHODS
References
Publications referenced by this paper.
SHOWING 1-10 OF 122 REFERENCES
Data Augmentation by Pairing Samples for Images Classification
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL
Dataset Augmentation in Feature Space
VIEW 23 EXCERPTS
HIGHLY INFLUENTIAL
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL
Gradient-based learning applied to document recognition
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL
AutoAugment: Learning Augmentation Policies from Data
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL
Data augmentation for improving deep learning in image classification problem
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL
Domain randomization for transferring deep neural networks from simulation to the real world
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL
Smart Augmentation Learning an Optimal Data Augmentation Strategy
VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL
The effectiveness of data augmentation in image classification using deep learning
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL
A Neural Algorithm of Artistic Style
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL