A Principled Approach to Unsupervised Anomaly Detection

arXiv:2609.21800 · cs.CV · Submitted 2026-09-18 · Read on arXiv

cs.CV

Submitted: 2026-09-18

Updated: 2026-09-18

Code: https://github.com/jgmyles/inverse-uad

Terminology

• Inverse Problem — 15× in this paper · explained in 2 episodes
• AUROC — 12× in this paper · explained in 2 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Latent Space — 3× in this paper · explained in 7 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Reconstruction Error — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Anatomical Tracings of Lesions After Stroke — 1× in this paper
• Anomaly Scoring Mechanism — 1× in this paper · explained in 1 episode
• Distillation — 1× in this paper · explained in 2 episodes
• Inverse Design — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes

Sources

• Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
• How well do generative models solve inverse problems? A benchmark study
• DINOv2: Learning Robust Visual Features without Supervision
• Wide Residual Networks

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