arXiv (All)
open access
Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detection
DINO models provide rich patch-level representations that have recently enabled strong performance in unsupervised anomaly detection (UAD). Most existing methods extract patch embeddings from normal images and model them independently, ignoring spatial and neighborhood relationships between patches.
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
- Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?#1062351
- Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?#1043104
- Unified Taxonomy for Multivariate Time Series Anomaly Detection using Deep Learning#1109024
- STGAD: Self-temporal generative adversarial framework with transformer attention for unsupervised multivariate time-series anomaly detection and localization.#218762
- MOPDA: Mixed-Trajectory On-Policy Distillation for Language-Guided Industrial Anomaly Detection#1128625
Record · ID 820376
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