Interactive Network Perturbation Between Teacher and Students for Semi-Supervised Semantic Segmentation

Abstract

The current golden standard of semi-supervised semantic segmentation is to generate and exploit pseudo-supervision on unlabeled images. This approach is however susceptible to the quality of pseudo-supervision--training often becomes unstable particularly at early stages and biased to incorrect supervision. To address these issues, we propose a new semi-supervised learning framework, dubbed Guided Pseudo Supervision (GPS). GPS comprises three networks, i.e., a teacher and two separate students. The teacher is first trained with a small set of labeled data and provides stable initial pseudo-supervision on the unlabeled data to the students. The students interactively train each other under the supervision of the teacher, and once they are sufficiently trained, they offer feedback supervision to the teacher so that the teacher improves in subsequent iterations. This strategy enables more stable and faster convergence than previous works, and consequently, GPS achieved state-of-the-art performance on Pascal VOC 2012 and Cityscapes datasets in various experiment settings.