mala-lab Goto Github PK
Name: Machine Learning & Applications (MaLA)
Type: Organization
Bio: A research team working with Guansong Pang, specializing in handling unknown or abnormal data instances
Location: Singapore
Name: Machine Learning & Applications (MaLA)
Type: Organization
Bio: A research team working with Guansong Pang, specializing in handling unknown or abnormal data instances
Location: Singapore
The official PyTorch implementation of Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment (AAAI2023, to appear).
ADer is an open source visual anomaly detection toolbox based on PyTorch, which supports multiple popular AD datasets and approaches.
Popular real-world datasets for anomaly detection on tabular data, graph data, image data, time series data, and video data
Official PyTorch implementation of the ICCV'23 paper “Anomaly Detection under Distribution Shift”
Official implementation of CVPR'24 paper 'Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection'.
Official implementation for paper "Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection" (ICLR 2024)
Source code of PRJ paper "Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds"
Official code for TNNLS paper "Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning"
Official repository for survey paper "Deep Graph Anomaly Detection: A Survey and New Perspectives", including diverse types of resources for graph anomaly detection.
Official implementation for "Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning" (AAAI'24)
Implementation of TKDE paper "Calibrated One-class classification-based Unsupervised Time series Anomaly detection"
Implementation of TKDE paper "Deep Isolation Forest for Anomaly Detection"
Code for Graph Continual Learning with Debiased Lossless Memory Replay (DeLoMe)
Official PyTorch implementation of the paper “Explainable Deep Few-shot Anomaly Detection with Deviation Networks”, weakly/partially supervised anomaly detection, few-shot anomaly detection, image defect detection.
Official implementation of KDD'19 paper "Deep Anomaly Detection with Deviation Networks"
Official PyTorch implementation of the ACM Multimedia 2024 paper “Improving Out-of-distribution Detection with Disentangled Foreground and Background Features”
Official PyTorch implementation of the paper “Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection”, open-set anomaly detection, few-shot anomaly detection, semi-supervised anomaly detection.
This code is for paper "Generative Semi-supervised Graph Anomaly Detection"
Implementation of CVPR'23 paper "Glocal Energy-based Learning for Few-Shot Open-Set Recognition"
Official implementation of the paper Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation(WSDM22)
Code for ECMLPKDD23 paper "Graph-level Anomaly Detection via Hierarchical Memory Networks" (HimNet)
Code Repository for Paper "HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks"
Official implementation of CVPR'24 paper 'Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts'.
Code Implementation of "Unsupervised Recognition of Unknown Objects for Open-World Object Detection"
The official implementation of CVPR 24' Paper "Learning Transferable Negative Prompts for Out-of-Distribution Detection"
Official code for paper "OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learning"
Top-tier conference papers on out-of-distribution detection
[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.
Official implementation of KDD'23 paper "Deep Weakly-supervised Anomaly Detection"
A declarative, efficient, and flexible JavaScript library for building user interfaces.
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An Open Source Machine Learning Framework for Everyone
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A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.