  This paper aims to address the problem of anomaly discovery in semantic
segmentation. Our key observation is that semantic classification plays a
critical role in existing approaches, while the incorrectly classified pixels
are easily regarded as anomalies. Such a phenomenon frequently appears and is
rarely discussed, which significantly reduces the performance of anomaly
discovery. To this end, we propose a novel Distillation Comparison Network
(DiCNet). It comprises of a teacher branch which is a semantic segmentation
network that removed the semantic classification head, and a student branch
that is distilled from the teacher branch through a distribution distillation.
We show that the distillation guarantees the semantic features of the two
branches hold consistency in the known classes, while reflect inconsistency in
the unknown class. Therefore, we leverage the semantic feature discrepancy
between the two branches to discover the anomalies. DiCNet abandons the
semantic classification head in the inference process, and hence significantly
alleviates the issue caused by incorrect semantic classification. Extensive
experimental results on StreetHazards dataset and BDD-Anomaly dataset are
conducted to verify the superior performance of DiCNet. In particular, DiCNet
obtains a 6.3% improvement in AUPR and a 5.2% improvement in FPR95 on
StreetHazards dataset, achieves a 4.2% improvement in AUPR and a 6.8%
improvement in FPR95 on BDD-Anomaly dataset. Codes are available at
https://github.com/zhouhuan-hust/DiCNet.
