Data Augmentation in Classification and Segmentation: A Survey and New Strategies.

ABSTRACT: In the past decade, deep neural networks, particularly convolutional neural networks, have revolutionised computer vision. However, all deep learning models may require a large amount of data so as to achieve satisfying results. Unfortunately, the availability of sufficient amounts of data for real-world problems is not always possible, and it is well recognised that a paucity of data easily results in overfitting. This issue may be addressed through several approaches, one of which is data augmentation. In this paper, we survey the existing data augmentation techniques in computer vision tasks, including segmentation and classification, and suggest new strategies. In particular, we introduce a way of implementing data augmentation by using local information in images. We propose a paramet

SUBMITTER: Alomar K 

PROVIDER: S-EPMC9966095 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature