Built independently by an author, for readers. Read the story and support ChapterPal
ChapterPal Logo
Log inSign up

keyword

noisy data

Noisy data refers to information that contains corrupted, irrelevant, or distorted values that obscure the true underlying patterns of a dataset. In computing, statistics, and machine learning, this noise can stem from hardware sensor faults, human measurement inaccuracies, transmission flaws, or incorrect data labels. When present during data analysis or model training, noisy data can distort statistical distributions, introduce bias, and cause predictive algorithms to learn random anomalies rather than genuine relationships. Handling noisy data typically involves preprocessing techniques such as outlier filtering, data cleaning, and robust learning methods designed to isolate meaningful signals from unwanted interference.

1 item

SoftPatch: Unsupervised Anomaly Detection with Noisy Data

SoftPatch: Unsupervised Anomaly Detection with Noisy Data

Xi Jiang, Jianlin Liu, Jinbao Wang, Qiang Nie, Kai Wu, Yong Liu, Chengjie Wang, Feng Zheng

OrganizationsDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyTencent

Why you should read this

Proposes SoftPatch, a patch-level denoising and memory re-weighting method that prevents defective training samples from distorting decision boundaries in real-world unsupervised anomaly detection.

Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper considers label-level noise in image sensory anomaly detection for the first time. To solve this problem, we proposed a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction. The scores are then stored in the memory bank to soften the anomaly detection boundary. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset. Comprehensive experiments in various noise scenes demonstrate that SoftPatch outperforms the state-of-the-art AD methods on the MVTecAD and BTAD benchmarks and is comparable to those methods under the setting without noise.

Added

2026-09-26