Comparing data augmentation strategies for deep image classification
McGuinness, KevinORCID: 0000-0003-1336-6477 and O'Gara, Sarah
(2019)
Comparing data augmentation strategies for deep image classification.
In: Irish Machine Vision and Image Processing Conference (IMVIP), 28-30 Aug 2019, Dublin, Ireland.
ISBN 978-0-9934207-4-0
Currently deep learning requires large volumes of training data to fit accurate models. In practice,
however, there is often insufficient training data available and augmentation is used to expand the dataset.
Historically, only simple forms of augmentation, such as cropping and horizontal flips, were used. More
complex augmentation methods have recently been developed, but it is still unclear which techniques are
most effective, and at what stage of the learning process they should be introduced. This paper investigates
data augmentation strategies for image classification, including the effectiveness of different forms of
augmentation, dependency on the number of training examples, and when augmentation should be introduced
during training. The most accurate results in all experiments are achieved using random erasing due to its
ability to simulate occlusion. As expected, reducing the number of training examples significantly increases
the importance of augmentation, but surprisingly the improvements in generalization from augmentation
do not appear to be only as a result of augmentation preventing overfitting. Results also indicate a learning
curriculum that injects augmentation after the initial learning phase has passed is more effective than the
standard practice of using augmentation throughout, and that injection too late also reduces accuracy. We find
that careful augmentation can improve accuracy by +2.83% to 95.85% using a ResNet model on CIFAR-10
with more dramatic improvements seen when there are fewer training examples. Source code is available at
https://git.io/fjPPy
Metadata
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Computer vision; deep learning; data augmentation; image classification; supervised learning; CNN; CIFAR-10