# Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data Source: [Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:45:08.233Z Authors: Guan-Rong Lu, Yueh-Cheng Liu, Tung-I Chen, Hung-Ting Su, Tsung-Han Wu, Winston H. Hsu Published: 2021-11-29T06:24:50.000Z ## Abstract Anomaly awareness is an essential capability for safety-critical applications such as autonomous driving. While recent progress of robotics and computer vision has enabled anomaly detection for image classification, anomaly detection on semantic segmentation is less explored. Conventional anomaly-aware systems assuming other existing classes as out-of-distribution (pseudo-unknown) classes for training a model will result in two drawbacks. (1) Unknown classes, which applications need to cope with, might not actually exist during training time. (2) Model performance would strongly rely on the class selection. Observing this, we propose a novel Synthetic-Unknown Data Generation, intending to tackle the anomaly-aware semantic segmentation task. We design a new Masked Gradient Update (MGU) module to generate auxiliary data along the boundary of in-distribution data points. In addition, we modify the traditional cross-entropy loss to emphasize the border data points. We reach the state-of-the-art performance on two anomaly segmentation datasets. Ablation studies also demonstrate the effectiveness of proposed modules. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()