Online-compatible Unsupervised Non-resonant Anomaly Detection Repository

Last update: Nov 09, 2021

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Overview

Online-compatible Unsupervised Non-resonant Anomaly Detection Repository

Repository containing all scripts used in the studies of Online-compatible Unsupervised Non-resonant Anomaly Detection model.

To train your own model, first Download the official dataset from zenodo and use the example code to prepare the datasets. To run the training, use:

python AE40Mhz.py [--single/--double/--supervised/--all] [--load] --out NAME

To train a single AE, the double + decorrelatied method, supervisedd, or all of them respectively. Trained model weights are also providedd in the weights folder that can be loaded using the --load flag.

The output of the script will create an NAME.h5 file in the base directory. Use this file to plot the results using the script plot.py

python plot.py --file NAME.h5

Different plot options are available in the script.

Owner

GitHub Repository

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Online-compatible Unsupervised Non-resonant Anomaly Detection Repository

Related tags

Overview

Online-compatible Unsupervised Non-resonant Anomaly Detection Repository

Owner

Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.

Code release of paper "Deep Multi-View Stereo gone wild"

Reproducing-BowNet: Learning Representations by Predicting Bags of Visual Words

Numerai tournament example scripts using NN and optuna

Code for NeurIPS 2021 paper: Invariant Causal Imitation Learning for Generalizable Policies

Deep Face Recognition in PyTorch

(CVPR 2021) Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

Official Implementation of SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations

MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification

Tensorflow 2 implementations of the C-SimCLR and C-BYOL self-supervised visual representation methods from "Compressive Visual Representations" (NeurIPS 2021)

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Pytorch Implementation for NeurIPS (oral) paper: Pixel Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation