Mask-based Data Augmentation for Semi-supervised Semantic Segmentation

Understand

Semantic segmentation using convolutional neural networks (CNN) is a crucial component in image analysis.

• Training a CNN to perform semantic segmentation requires a large amount of labeled data, where the production of such labeled data is both costly and labor intensive.
• Semi-supervised learning algorithms address this issue by utilizing unlabeled data and so reduce the amount of labeled data needed for training.
• In particular, data augmentation techniques such as CutMix and ClassMix generate additional training data from existing labeled data.