arXiv Machine Learning July 27, 2026 By Alireza Dastmalchi Saei, Shervin Rahimzadeh Arashloo

Deep Convolutional Large-Margin $\ell_p$-SVDD for Visual Anomaly Detection

Read the original on arXiv Machine Learning →

arXiv:2607. 22212v1 Announce Type: cross Abstract: Visual anomaly detection requires adaptive representations and reliable decision boundaries, particularly when anomalous training samples are scarce and class distributions are highly imbalanced.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

benchmarks

arXiv AI

Jun 11

OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection

arXiv:2507. 21164v2 Announce Type: replace-cross Abstract: Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where anomalous samples are rare or not available.

By Nicolas Pinon (MYRIAD), Robin Trombetta (MYRIAD), Carole Lartizien (MYRIAD)

benchmarks

arXiv AI

Jul 28

DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection

arXiv:2607. 23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD).

By Jyun-Ze Tang, Po-Han Huang, Ming-Ching Chang, Chih-Fan Hsu, Jeng-Lin Li

llms rag benchmarks

Hugging Face Trending Papers

Jul 27

DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection

Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent local patch features, leaving the global contextual information encoded by Vision Transformers (ViTs) underexploited.

llms rag benchmarks

Hugging Face Trending Papers

Jun 29

UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction

Unified visual anomaly detection seeks to train a single detector that can be deployed across categories, domains, and application scenarios. In the few-shot transfer regime, the key challenge is to estimate an episode-specific boundary for an unseen target category from a small support set.

arXiv AI

Jul 24

CLOE: Christoffel Loss Autoencoder for Anomaly Detection

arXiv:2607. 20530v1 Announce Type: cross Abstract: Semi-supervised anomaly detection plays a key role in diverse fields such as process monitoring, healthcare, and finance.

By L\'ea Billet (LAAS, INSA Toulouse, ANITI), Louise Trav\'e-Massuy\`es (LAAS-DISCO, Comue de Toulouse, ANITI), Elodie Chanthery (LAAS), Alexandre Gaffet

diffusion benchmarks

arXiv Machine Learning

Jun 19

We Need to Rethink Benchmarking in Anomaly Detection

arXiv:2507. 15584v2 Announce Type: replace Abstract: Despite the continuous proposal of new anomaly detection algorithms and extensive benchmarking efforts, progress seems to stagnate, with only minor performance differences between established baselines and new algorithms.

By Philipp R\"ochner, Simon Kl\"uttermann, Kevin Kammler, Franz Rothlauf, Emmanuel M\"uller, Daniel Schl\"or

benchmarks

Related stories

OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection

DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection

DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection

UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction

CLOE: Christoffel Loss Autoencoder for Anomaly Detection

We Need to Rethink Benchmarking in Anomaly Detection