  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. In this paper we propose a new approach for data augmentation,
termed ComplexMix, which incorporates aspects of CutMix and ClassMix with
improved performance. The proposed approach has the ability to control the
complexity of the augmented data while attempting to be semantically-correct
and address the tradeoff between complexity and correctness. The proposed
ComplexMix approach is evaluated on a standard dataset for semantic
segmentation and compared to other state-of-the-art techniques. Experimental
results show that our method yields improvement over state-of-the-art methods
on standard datasets for semantic image segmentation.
