USAD - UnSupervised Anomaly Detection on multivariate time series

Last update: Jan 04, 2023

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Deep Learning usad

Overview

USAD - UnSupervised Anomaly Detection on multivariate time series

Scripts and utility programs for implementing the USAD architecture.

Implementation by: Francesco Galati.

Additional contributions: Julien Audibert, Maria A. Zuluaga.

How to cite

If you use this software, please cite the following paper as appropriate:

Audibert, J., Michiardi, P., Guyard, F., Marti, S., Zuluaga, M. A. (2020).
USAD : UnSupervised Anomaly Detection on multivariate time series.
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, August 23-27, 2020

Requirements

PyTorch 1.6.0
CUDA 10.1 (to allow use of GPU, not compulsory)

Running the Software

All the python classes and functions strictly needed to implement the USAD architecture can be found in usad.py. An example of an application deployed with the SWaT dataset is included in USAD.ipynb.

Copyright and licensing

This software is released under the BSD-3 license. Please see the license file_ for details.

Publication

Audibert et al. USAD : UnSupervised Anomaly Detection on multivariate time series. 2020

USAD - UnSupervised Anomaly Detection on multivariate time series

Related tags

Overview

USAD - UnSupervised Anomaly Detection on multivariate time series

How to cite

Requirements

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USAD - UnSupervised Anomaly Detection on multivariate time series

Related tags

Overview

USAD - UnSupervised Anomaly Detection on multivariate time series

How to cite

Requirements

Running the Software

Copyright and licensing

Publication

Owner

Repository for scripts and notebooks from the book: Programming PyTorch for Deep Learning

Federated Learning - Including common test models for federated learning, like CNN, Resnet18 and lstm, controlled by different parser

IMBENS: class-imbalanced ensemble learning in Python.

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SMIS - Semantically Multi-modal Image Synthesis(CVPR 2020)

​ This is the Pytorch implementation of Progressive Attentional Manifold Alignment.

PyTorch implementation of ICLR 2022 paper PiCO: Contrastive Label Disambiguation for Partial Label Learning

A Collection of Papers and Codes for ICCV2021 Low Level Vision and Image Generation

Multiple-Object Tracking with Transformer

The MATH Dataset

A python library for highly configurable transformers - easing model architecture search and experimentation.

D-NeRF: Neural Radiance Fields for Dynamic Scenes

MogFace: Towards a Deeper Appreciation on Face Detection

Waymo motion prediction challenge 2021: 3rd place solution

UnpNet - Rethinking 3-D LiDAR Point Cloud Segmentation(IEEE TNNLS)

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