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

Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression.

Lightweight stereo matching network based on MobileNetV1 and MobileNetV2

Pytorch implementation of PCT: Point Cloud Transformer

Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

Instance-Dependent Partial Label Learning

An implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional Neural Network"

Code for "Learning Graph Cellular Automata"

Official Repository for our ICCV2021 paper: Continual Learning on Noisy Data Streams via Self-Purified Replay

Official PyTorch implementation and pretrained models of the paper Self-Supervised Classification Network

Code for pre-training CharacterBERT models (as well as BERT models).

Code for "Offline Meta-Reinforcement Learning with Advantage Weighting" [ICML 2021]

Making Structure-from-Motion (COLMAP) more robust to symmetries and duplicated structures

Generate vibrant and detailed images using only text.

Official Implementation for Fast Training of Neural Lumigraph Representations using Meta Learning.

Direct design of biquad filter cascades with deep learning by sampling random polynomials.

Pytorch implementation of the paper "COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking."

Evaluating AlexNet features at various depths

An example showing how to use jax to train resnet50 on multi-node multi-GPU

Official code for CVPR2022 paper: Depth-Aware Generative Adversarial Network for Talking Head Video Generation