A Preliminary Study on Data Augmentation of Deep Learning for Image Classification

Abstract

Deep learning models have a large number of freeparameters that need to be calculated by effective trainingof the models on a great deal of training data to improvetheir generalization performance. However, data obtaining andlabeling is expensive in practice. Data augmentation is one of themethods to alleviate this problem. In this paper, we conduct apreliminary study on how three variables (augmentation method,augmentation rate and size of basic dataset per label) can affectthe accuracy of deep learning for image classification. The studyprovides some guidelines: (1) it is better to use transformationsthat alter the geometry of the images rather than those justlighting and color. (2) 2-3 times augmentation rate is good enoughfor training. (3) the smaller amount of data, the more obviouscontributions could have.

4 pages, 4 figures

References in corpus (8)

• Improved Regularization of Convolutional Neural Networks with Cutout
• Random Erasing Data Augmentation
• AutoAugment: Learning Augmentation Policies from Data
• Recent Advances in Recurrent Neural Networks
• Improving Deep Learning using Generic Data Augmentation
• DeepGini: Prioritizing Massive Tests to Enhance the Robustness of Deep Neural Networks
• Regularization for Unsupervised Deep Neural Nets
• NeuralVis: Visualizing and Interpreting Deep Learning Models