Self-Supervised Anomaly Detection from Anomalous Training Data via Iterative Latent Token Masking-Reference-Cited by-同舟云学术

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Self-Supervised Anomaly Detection from Anomalous Training Data via Iterative Latent Token Masking

Published:2023-10-02 Issue: Volume: Page:
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Container-title:2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
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Author:

Patel Ashay¹,Tudosiu Petru-Daniel¹,Pinaya Walter H.L.¹,Graham Mark S.¹,Adeleke Olusola¹,Cook Gary¹,Goh Vicky¹,Ourselin Sebastien¹,Cardoso M. Jorge¹

Affiliation:

1. King’s College,London

Funder

Health

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10350357/10350360/10350925.pdf?arnumber=10350925

Reference29 articles.

1. Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study

2. Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images

3. MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

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1. VDKD: A ViT-Based Student-Teacher Knowledge Distillation for Multi-Texture Class Anomaly Detection;2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS);2024-05-17

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