#Anomaly Detection

PyOD provides a Python library for detecting anomalies in multivariate datasets with over 50 algorithms, ranging from classical methods like LOF to advanced neural networks. The library features a user-friendly interface and efficient processing, enhanced by numba and joblib for fast training and prediction. Suitable for diverse applications, PyOD has gained over 22 million downloads and supports integration with distributed systems such as Databricks. Comprehensive documentation is available to guide users in implementing effective outlier detection solutions.

Explore a new approach to zero-shot anomaly segmentation without additional training through hybrid prompt regularization combined with existing foundation models. Improve anomaly detection using models like Grounding DINO and Segment Anything. This repository features user-friendly demos available on Colab and Huggingface, showcasing the efficacy of the SAA+ framework on datasets such as MVTec-AD, VisA, KSDD2, and MTD. SAA+ provides optimal anomaly identification with minimal setup, catering to computer vision researchers and developers. Discover recent advancements and the work that led to success at the VAND workshop.

Explore a curated collection of scholarly articles on diffusion models in medical imaging, featuring survey papers, challenges, and applications including anomaly detection and image restoration. This project compiles influential publications from conferences and journals like Medical Image Analysis and MICCAI 2023, serving as a valuable resource for professionals seeking the latest advancements in diffusion model applications.

FastSAM is an image segmentation model offering 50 times faster performance with limited data. It supports text, box, and point prompts for a user-friendly experience. The model is lightweight for efficient memory usage, and recent updates improve edge quality and add semantic labels. Available demos on HuggingFace and Replicate showcase its use in anomaly detection and more.

OATS delivers a reliable time series anomaly detection system utilizing advanced methods. It supports univariate and multivariate data, providing consistent outputs across models. Its modular structure facilitates integration into diverse projects. Key features include user-friendly model interfaces, options for setting prediction thresholds, and compatibility with deep learning frameworks such as PyTorch and TensorFlow. The project invites open-source contributions, with comprehensive documentation available to support setup and implementation for enhanced detection adaptability.

Discover a wide range of resources on Transformers for time series, which outlines recent innovations in the area. This well-curated repository grants access to influential papers, codes, and datasets aimed at advancing the comprehension and modeling of time series using Transformers. Regularly updated, the repository addresses various uses such as forecasting, anomaly detection, and classification. Contributions of new resources or error corrections are welcomed. This repository serves as an important resource for AI specialists interested in time series analysis, delivering insights from leading AI conferences and journals. It aids ongoing AI research and development for time series, supplying essential tools and references for both novices and experienced researchers.

The Anomaly Transformer project presents a novel method for detecting anomalies in time series data through association discrepancy. It incorporates an Anomaly-Attention mechanism and a minimax strategy to distinguish between normal and abnormal data effectively. Pre-processed datasets and experiment scripts are included for reproducibility of results.

Merlion is a machine learning library designed for time series analysis, focusing on forecasting, anomaly detection, and change point detection. It supports both univariate and multivariate datasets with features like standardized data loading and diverse model options, including statistical, ensemble, and deep learning methods, as well as AutoML for effective hyperparameter tuning. The library offers a straightforward interface with visualizations and scalable distributed computation for industrial applications. Its evaluation pipeline mimics real-world conditions, supporting benchmarking and enhanced model performance. Simple installation and detailed documentation make it suitable for engineers and researchers developing custom models.