DISSERTATION

Few-Shot Classification in Deep Learning based Anomaly Detection of Noisy Industrial Data

Keywords:
Deep learning Anomaly detection Pattern recognition (psychology) Noise (video) Artificial neural network Feature (linguistics)

Metrics

0
Cited By
0.00
FWCI (Field Weighted Citation Impact)
0
Refs
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Tree-ring climate responses
Physical Sciences →  Earth and Planetary Sciences →  Atmospheric Science
Forest Ecology and Biodiversity Studies
Life Sciences →  Agricultural and Biological Sciences →  Insect Science
Archaeological Research and Protection
Physical Sciences →  Earth and Planetary Sciences →  Space and Planetary Science

Related Documents

JOURNAL ARTICLE

AnomalyNLP: Noisy-Label Prompt Learning for Few-Shot Industrial Anomaly Detection

Hua LiJin Qian

Journal:   Electronics Year: 2025 Vol: 14 (20)Pages: 4016-4016
JOURNAL ARTICLE

Few-shot Anomaly Detection and Classification Through Reinforced Data Selection

Xiao HanDepeng XuShuhan YuanXintao Wu

Journal:   2022 IEEE International Conference on Data Mining (ICDM) Year: 2022 Pages: 963-968
JOURNAL ARTICLE

Learning Noisy Few-Shot Classification Without Relying on Pseudo-Noise Data

Yixin WuHui XueYuexuan AnPengfei Fang

Journal:   IEEE Signal Processing Letters Year: 2024 Vol: 32 Pages: 86-90
© 2026 ScienceGate Book Chapters — All rights reserved.