arXiv (All)
open access
InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection
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 fr
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Survey on Industrial Anomaly Synthesis#1248247
- Unsupervised visual anomaly detection in genuine industrial production environments#124443
- CORE: Contrast-and-Reflect for Self-Supervised Prompt Optimization in Anomaly Synthesis#1354300
- Few-Shot Adaptation for Anomalous Sound Detection at Low False-Positive Rate#1354575
- Is In-Domain Training Enough for Fine-Grained Industrial Anomaly Understanding?#1249734
Record · ID 1325928
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive