awesome-anomaly-detection

Why we included this project

Anomaly detection is not one problem, and this curated index is a practical way to map the field before committing to a method. It collects papers and code organized by the data types that actually change how you approach the task: time-series, video, and image. Image work gets split further into classification, out-of-distribution detection, and segmentation, a distinction that matters when you are scoping a project. The survey-paper section gives newcomers a clear reading path into deep and shallow approaches, while practitioners can jump straight to the code links for a specific technique. It is a reference list rather than runnable software, so treat it as a starting point for finding the primary sources and implementations you will actually evaluate.