2021/11/29 by Guan-Rong Lu, Lu, Guan-Rong, Yueh-Cheng Liu +9
Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Anomaly (physics) #Anomaly Detection Techniques and Applications #Anomaly detection #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Data mining #Entropy (arrow of time) #FOS: Computer and information sciences #Machine learning #Pattern recognition (psychology) #Segmentation #cs.CV
paper · pdf · doi:10.48550/arxiv.2111.14343
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/11/29 · openalex publication_date 2021/11/29 · arxiv updated 2021/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.