In this paper, we aim to improve the performance of semantic image segmentation in a semi-supervised setting where training is performed with a reduced set of annotated images and additional non-annotated images. We present a method based on an ensemble of deep segmentation models. Models are trained on subsets of the annotated data and use non-annotated images to exchange information with each other, similar to co-training. Diversity across models is enforced with the use of adversarial samples. We demonstrate the potential of our method on two challenging image segmentation problems, and illustrate its ability to share information between simultaneously trained models, while preserving their diversity. Results indicate clear advantages in terms of performance compared to recently proposed semi-supervised methods for segmentation.

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