Machine Learning & Applications (MaLA)'s repositories
TAM-master
Official implementation of NeurIPS'23 paper "Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection"
out-of-distribution-detection-resources
Top-tier conference papers on out-of-distribution detection
ADRepository-Anomaly-detection-datasets
Popular real-world datasets for anomaly detection on tabular data, graph data, image data, time series data, and video data
AnomalyCLIP
Official implementation for paper "Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection" (ICLR 2024)
Awesome-Deep-Graph-Anomaly-Detection
A repository for resources of deep learning-based graph anomaly detection.
ACT
The official PyTorch implementation of Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment (AAAI2023, to appear).
Glocal
Implementation of CVPR'23 paper "Glocal Energy-based Learning for Few-Shot Open-Set Recognition"
PEBAL
[ECCV'22 Oral] Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes. Dealing with out-of-distribution detection or open-set recognition in semantic segmentation.
RPL
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
weakly-polyp
[MICCAI'22] Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection.
ADer
ADer is an open source visual anomaly detection toolbox based on PyTorch, which supports multiple popular AD datasets and approaches.
ASE
Source code of PRJ paper "Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds"
deep-iforest
Implementation of "Deep Isolation Forest for Anomaly Detection"
HimNet
Code for ECMLPKDD23 paper "Graph-level Anomaly Detection via Hierarchical Memory Networks" (HimNet)
HRGCN
Code Repository for Paper "HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks"
mepu-owod
Code Implementation of "Unsupervised Recognition of Unknown Objects for Open-World Object Detection"