Inconsistency Masks: Harnessing Model Disagreement for Stable Semi-Supervised Segmentation

Abstract

A primary challenge in semi-supervised learning (SSL) for segmentation is the confirmation bias from noisy pseudo-labels, which destabilizes training and degrades performance. We propose Inconsistency Masks (IM), a framework that reframes model disagreement not as noise to be averaged away, but as a valuable signal for identifying uncertainty. IM leverages an ensemble of teacher models to generate a mask that explicitly delineates regions where predictions diverge. By filtering these inconsistent areas from input-pseudo-label pairs, our method effectively mitigates the cycle of error propagation common in both continuous and iterative self-training paradigms. Extensive experiments on the Cityscapes benchmark demonstrate IM's effectiveness as a general enhancement framework: when paired with leading approaches like iMAS, U$^2$PL, and UniMatch, our method consistently boosts accuracy, achieving superior benchmarks across ResNet-50 and DINOv2 backbones, and even improving distilled architectures like SegKC. Furthermore, the method's robustness is confirmed in resource-constrained scenarios where pre-trained weights are unavailable. On three additional diverse datasets from medical and underwater domains trained entirely from scratch, IM significantly outperforms standard SSL baselines. Notably, the IM framework is dataset-agnostic, seamlessly handling binary, multi-class, and complex multi-label tasks by operating on discretized predictions. By prioritizing training stability, IM offers a generalizable and robust solution for semi-supervised segmentation, particularly in specialized areas lacking large-scale pre-training data. The full code is available at: https://github.com/MichaelVorndran/InconsistencyMasks