ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2403.01859
  4. Cited By
CSE: Surface Anomaly Detection with Contrastively Selected Embedding

CSE: Surface Anomaly Detection with Contrastively Selected Embedding

4 March 2024
Simon Thomine
H. Snoussi
ArXivPDFHTML

Papers citing "CSE: Surface Anomaly Detection with Contrastively Selected Embedding"

10 / 10 papers shown
Title
ADBench: Anomaly Detection Benchmark
ADBench: Anomaly Detection Benchmark
Songqiao Han
Xiyang Hu
Hailiang Huang
Mingqi Jiang
Yue Zhao
OOD
72
306
0
19 Jun 2022
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented
  Anomaly Localization
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization
Sungwook Lee
Seunghyun Lee
B. Song
99
194
0
09 Jun 2022
Generative Adversarial Networks
Generative Adversarial Networks
Gilad Cohen
Raja Giryes
GAN
280
30,103
0
01 Mar 2022
FastFlow: Unsupervised Anomaly Detection and Localization via 2D
  Normalizing Flows
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
Jiawei Yu1
Ye Zheng
Xiang Wang
Wei Li
Yushuang Wu
Rui Zhao
Liwei Wu
61
311
0
15 Nov 2021
Student-Teacher Feature Pyramid Matching for Anomaly Detection
Student-Teacher Feature Pyramid Matching for Anomaly Detection
Guodong Wang
Shumin Han
Errui Ding
Di Huang
71
219
0
07 Mar 2021
G2D: Generate to Detect Anomaly
G2D: Generate to Detect Anomaly
M. PourReza
Bahram Mohammadi
Mostafa Khaki
Samir Bouindour
H. Snoussi
Mohammad Sabokrou
53
63
0
20 Jun 2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan
Quoc V. Le
3DV
MedIm
139
18,134
0
28 May 2019
GEE: A Gradient-based Explainable Variational Autoencoder for Network
  Anomaly Detection
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
Q. Nguyen
Kar Wai Lim
D. Divakaran
K. H. Low
M. Chan
DRL
45
136
0
15 Mar 2019
A disciplined approach to neural network hyper-parameters: Part 1 --
  learning rate, batch size, momentum, and weight decay
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
L. Smith
281
1,033
0
26 Mar 2018
Describing Textures in the Wild
Describing Textures in the Wild
Mircea Cimpoi
Subhransu Maji
Iasonas Kokkinos
S. Mohamed
Andrea Vedaldi
3DV
130
2,674
0
14 Nov 2013
1