A curated list of papers & resources on anomaly detection foundation models using large language model, vision-language model, graph foundation model, time series foundation model, etc

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Official repository for 2025 TKDE survey paper "Deep Graph Anomaly Detection: A Survey and New Perspectives", including diverse types of resources for graph anomaly detection

Time-Series Anomaly Detection Comprehensive Benchmark

Official implementation for NeurIPS'24 paper "Generative Semi-supervised Graph Anomaly Detection"

Official implementation of NeurIPS'23 paper "Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection"

Official implementation of KDD'23 paper "Deep Weakly-supervised Anomaly Detection"

[CVPR 2023] This is the official PyTorch implementation for "Dynamic Focus-aware Positional Queries for Semantic Segmentation".

This is the official PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

[NeurIPS 2022 Spotlight] This is the official PyTorch implementation of "EcoFormer: Energy-Saving Attention with Linear Complexity"

[NeurIPS 2022 Spotlight] This is the official PyTorch implementation of "Fast Vision Transformers with HiLo Attention"

The official PyTorch implementation of Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment (AAAI2023, to appear).

Inference-based time-resolved whole-brain imaging

Collections of model quantization algorithms. Any issues, please contact Peng Chen ([email protected])

[ICCV 2021] Official implementation of "Scalable Vision Transformers with Hierarchical Pooling"

This is the official PyTorch implementation for "Mesa: A Memory-saving Training Framework for Transformers".

[TPAMI 2024] This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.

[AAAI 2021] (oral) Progressive One-shot Human Parsing, [TPAMI 2023] End-to-end One-shot Human Parsing

[AAAI 2022] This is the official PyTorch implementation of "Less is More: Pay Less Attention in Vision Transformers"