InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection
[Uczenie maszynowe] Autorzy: Łukasz Marcjan, Dorota Wilk-Kołodziejczyk, Roberto Corizzo
Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in which inspection systems must be extended from one industrial component to another over time. In our setting, continual industrial anomaly detection is framed as a task-incremental problem: each component defines a task, while the learning objective remains semi-supervised. The goal extends beyond conventional anomaly detection, since models should preserve their performance on all components as they are sequentially exposed to new components. This framing is especially relevant because many existing industrial pipelines either assume fully supervised defect labels or study only isolated datasets, leaving open the question of how semi-supervised anomaly detectors behave across a sequence of heterogeneous components. We address this gap by orga…
- Kategoria arxiv
- cs.LG
- Kategoria
- Uczenie maszynowe