Neural-Collapse-guided Task-Free Continual Anomaly Detection
Recent years have witnessed growing interest in continual anomaly detection for industrial visual inspection. However, real-world manufacturing environments exhibit unpredictable shifts in data distributions, rendering task-dependent continual learning assumptions impractical. To address this limitation, we formulate industrial anomaly detection as a task-free continual learning problem and propose NC-TFAD, a neural-collapse-inspired, geometry-driven framework for learning from non-stationary data streams without task boundaries. NC-TFAD freezes a pretrained backbone and aligns streaming features to a simplex Equiangular Tight Frame (ETF) prototype space to stabilize representation geomet...
| Authors | Xiaotong Kong, Chaoyang Song, Ziai Zhou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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