Structured Consistency Loss for semi-supervised semantic segmentation

Understand

The consistency loss has played a key role in solving problems in recent studies on semi-supervised learning.

• Yet extant studies with the consistency loss are limited to its application to classification tasks; extant studies on semi-supervised semantic segmentation rely on pixel-wise classification, which does not reflect the structured nature of characteristics in prediction.
• We propose a structured consistency loss to address this limitation of extant studies.
• Structured consistency loss promotes consistency in inter-pixel similarity between teacher and student networks.