Skip to content
Universidad del Mar SIBUMAR Descubridor Institucional UMAR
  • Inicio
  • Búsqueda avanzada
  • Explorar
  • Login
    • English
    • Deutsch
    • Español
    • Français
    • Italiano
Advanced
  • SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Cover Image

SoftPatch: Unsupervised Anomaly Detection with Noisy Data

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Jiang, Xi, Chen, Ying, Nie, Qiang, Liu, Yong, Liu, Jianlin, Gao, Bin-Bin, Liu, Jun, Wang, Chengjie, Zheng, Feng
Format: Preprint
Published: 2024
Subjects:
Computer Vision and Pattern Recognition
Artificial Intelligence
Machine Learning
Online Access:
Acceder al recurso
Tags: Add Tag
No Tags, Be the first to tag this record!
  • Cite this
  • Text this
  • Email this
  • Print
  • Export Record
    • Export to RefWorks
    • Export to EndNoteWeb
    • Export to EndNote
  • Save to List
  • Permanent link
  • Holdings
  • Description
  • Comments
  • Similar Items
  • Staff View

Internet

https://arxiv.org/abs/2403.14233

Similar Items

  • SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation
    by: Wang, Chengjie, et al.
    Published: (2024)
  • Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
    by: Jiang, Xi, et al.
    Published: (2024)
  • Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
    by: Liu, Jiaqi, et al.
    Published: (2024)
  • AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison
    by: Jiang, Xi, et al.
    Published: (2026)
  • One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection
    by: Gao, Bin-Bin, et al.
    Published: (2026)
Universidad del Mar
Universidad del MarSistema Bibliotecario de la Universidad del MarDescubridor Institucional UMARImplementación y desarrollo: Mtro. Carlos Alonso Albores Pérez
InicioBúsqueda avanzadaExplorar
Visitas al Descubridor: 33,245© 2026 Universidad del Mar