anomaly-generation

[ECCV 2024] Few-Shot Anomaly-Driven Generation for Anomaly Detection

Repository for ‘Anomaly Detection and Generation with Diffusion Models: A Survey’.

This repository provides a hierarchical taxonomy of key paperson anomaly generation methods, surpassing flat lists with fine-grained subcategories that delineate emerging hotspots

This repository provides a hierarchical taxonomy of key paperson computer vision methods, surpassing flat lists with fine-grained subcategories that delineate emerging hotspots

SigVarGen is a Python framework for time-series signal generation, data augmentation, and anomaly simulation. It creates diverse 1D signal variants under controlled conditions, including idle-state, perturbed, and noisy signals.

Reference Anomaly Generation and Segmentation

A “Generate Once, Synthesize Many” Framework for Synthetic Anomaly Generation in Industrial Anomaly Detection.

Unofficial implementation for paper (Large-Scale Universal Defect Generation : Foundation Models and Datasets)

Experimental repository for a research paper on open-source CLI tools for DDoS simulation in network and transport layers (L3/L4) in Internet environments. The study evaluates flood performance and traffic anomaly generation, while this repository provides all codes, commands, and scripts used to perform these comparative tests.