anomaly-detection

OpenChatBI is an intelligent chat-based BI tool powered by large language models, designed to help users query, analyze, and visualize data through natural language conversations. It uses LangGraph and LangChain to build chat agent and workflows that support natural language to SQL conversion and data analysis.

AI agent for Home Assistant — talk to your home, create automations in plain language, analyze cameras with face recognition, and get proactive anomaly alerts. Cloud LLMs or fully local via Ollama.

A modular, skill-based autonomous Security Operations Center (SOC) agent that monitors OpenSearch/Elasticsearch data, builds RAG-based behavioral memory, and validates real-time anomalies using LLMs.

PyTorch implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

UniMMAD: Unified Multi-Modal and Multi-Class Anomaly Detection via MoE-Driven Feature Decompression

Reduce logs to their semantic anomalies

autoupdate paper list

Awesome-3D/Multimodal-Anomaly-Detection-and-Localization/Segmentation/3D-KD/3D-knowledge-distillation

[ICCV'23 Oral] Unmasking Anomalies in Road-Scene Segmentation

LLM-powered security log analyzer: detect threats & anomalies with zero regex — just declare a Pydantic schema. Real-time Telegram alerts, SIEM-ready with Elasticsearch/Kibana. Supports OpenAI, Ollama, vLLM.

This includes the codebase for EntroPE (Entropy-Guided Dynamic Patch Encoder for Time Series Forecasting) paper.

This is a cross-modal benchmark for industrial anomaly detection.

A set of jupyter notebooks

[AAAI 2026] Offical implementation of "Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory".

VAAS is an inference-first, research-driven library for image integrity analysis. It integrates Vision Transformer Attention Mechanisms with patch-level self-consistency analysis to enable fine-grained localization and detection of visual inconsistencies across diverse image analysis tasks.

[AAAI'25] The implementation of paper "Federated Foundation Models on Heterogeneous Time Series" | The first work to explore time series foundation models on federated setting.

This project aims to develop an innovative anomaly detection system using advanced data mining and deep learning techniques to accurately identify and localize defects in manufacturing components, thereby enhancing quality control processes and reducing production losses.