Code for paper entitled "Improving Novelty Detection using the Reconstructions of Nearest Neighbours"

Related tags

Deep LearningNLN
Overview

NLN: Nearest-Latent-Neighbours

A repository containing the implementation of the paper entitled Improving Novelty Detection using the Reconstructions of Nearest Neighbours

Installation

Install conda environment by:

    conda create --name nln python=3.7

Run conda environment by:

    conda activate nln

Install dependancies by running:

    pip install -r dependancies

Additionally for training on a GPU run:

    conda install -c anaconda tensorflow-gpu=2.2.0

Replication of results in paper

Run the following to replicate the results for MNIST, CIFAR-10, Fashion-MNIST and MVTec-AD respectively

    sh experiments/run_mnist.sh
    sh experiments/run_cifar.sh
    sh experiments/run_fmnist.sh
    sh experiments/run_mvtec.sh

Or to execute all experiments sequentially the following script can be run:

    sh experiments/run_all.sh

MVTec-AD usage

You will need to download the MVTec anomaly detection dataset and specify the its path using -mvtec_path command line option.

Training

Run the following:

    python main.py -anomaly_class <0,1,2,3,4,5,6,7,8,9,bottle,cable,...> \
                   -percentage_anomaly <float> \
                   -limit <int> \
                   -epochs <int> \
                   -latent_dim <int> \
                   -data <MNIST,FASHION_MNIST,CIFAR10,MVTEC> \
                   -mvtec_path <str>\
                   -neighbors <int(s)> \
                   -algorithm <knn> \
		   -patches <True, False> \
		   -crop <True, False> \
		   -rotate <True, False> \
		   -patch_x <int> \    
		   -patch_y <int> \    
		   -patch_x_stride <int> \    
		   -patch_y_stride <int> \    
		   -crop_x <int> \    
		   -crop_y <int> \

Reporting Results

Run the following given the correctly generated results files:

    python report.py -data <MNIST,CIFAR10,FASHION_MNIST,MVTEC> -seed <filepath-seed>

Licensing

Source code of NLN is licensed under the MIT License.

Owner
Michael (Misha) Mesarcik
Electrical and Computer Engineer
Michael (Misha) Mesarcik
GitHub Repository https://arxiv.org/abs/2111.06150
The official implementation of ELSA: Enhanced Local Self-Attention for Vision Transformer

ELSA: Enhanced Local Self-Attention for Vision Transformer By Jingkai Zhou, Pich

DamoCV 87 Dec 19, 2022
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models

DSEE Codes for [Preprint] DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models Xuxi Chen, Tianlong Chen, Yu Cheng, Weizhu Ch

VITA 4 Dec 27, 2021
Using Python to Play Cyberpunk 2077

CyberPython 2077 Using Python to Play Cyberpunk 2077 This repo will contain code from the Cyberpython 2077 video series on Youtube (youtube.

Harrison 118 Oct 18, 2022
GeoTransformer - Geometric Transformer for Fast and Robust Point Cloud Registration

Geometric Transformer for Fast and Robust Point Cloud Registration PyTorch imple

Zheng Qin 220 Jan 05, 2023
Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

CoaDTI Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2 Abstract Environment The test was conducted i

Layne_Huang 7 Nov 14, 2022
A flexible ML framework built to simplify medical image reconstruction and analysis experimentation.

meddlr Getting Started Meddlr is a config-driven ML framework built to simplify medical image reconstruction and analysis problems. Installation To av

Arjun Desai 36 Dec 16, 2022
Detection of drones using their thermal signatures from thermal camera through YOLO-V3 based CNN with modifications to encapsulate drone motion

Drone Detection using Thermal Signature This repository highlights the work for night-time drone detection using a using an Optris PI Lightweight ther

Chong Yu Quan 6 Dec 31, 2022
Implementation of CVPR 2021 paper "Spatially-invariant Style-codes Controlled Makeup Transfer"

SCGAN Implementation of CVPR 2021 paper "Spatially-invariant Style-codes Controlled Makeup Transfer" Prepare The pre-trained model is avaiable at http

118 Dec 12, 2022
Implementation of Bottleneck Transformer in Pytorch

Bottleneck Transformer - Pytorch Implementation of Bottleneck Transformer, SotA visual recognition model with convolution + attention that outperforms

Phil Wang 621 Jan 06, 2023
SynNet - synthetic tree generation using neural networks

SynNet This repo contains the code and analysis scripts for our amortized approach to synthetic tree generation using neural networks. Our model can s

Wenhao Gao 60 Dec 29, 2022
Allows including an action inside another action (by preprocessing the Yaml file). This is how composite actions should have worked.

actions-includes Allows including an action inside another action (by preprocessing the Yaml file). Instead of using uses or run in your action step,

Tim Ansell 70 Nov 04, 2022
Implémentation en pyhton de l'article Depixelizing pixel art de Johannes Kopf et Dani Lischinski

Implémentation en pyhton de l'article Depixelizing pixel art de Johannes Kopf et Dani Lischinski

TableauBits 3 May 29, 2022
Python TFLite scripts for detecting objects of any class in an image without knowing their label.

Python TFLite scripts for detecting objects of any class in an image without knowing their label.

Ibai Gorordo 42 Oct 07, 2022
Code for Multiple Instance Active Learning for Object Detection, CVPR 2021

MI-AOD Language: 简体中文 | English Introduction This is the code for Multiple Instance Active Learning for Object Detection (The PDF is not available tem

Tianning Yuan 269 Dec 21, 2022
DFM: A Performance Baseline for Deep Feature Matching

DFM: A Performance Baseline for Deep Feature Matching Python (Pytorch) and Matlab (MatConvNet) implementations of our paper DFM: A Performance Baselin

143 Jan 02, 2023
Code for "Learning to Segment Rigid Motions from Two Frames".

rigidmask Code for "Learning to Segment Rigid Motions from Two Frames". ** This is a partial release with inference and evaluation code.

Gengshan Yang 157 Nov 21, 2022
Scalable, event-driven, deep-learning-friendly backtesting library

...Minimizing the mean square error on future experience. - Richard S. Sutton BTGym Scalable event-driven RL-friendly backtesting library. Build on

Andrew 922 Dec 27, 2022
Official implementation of "Robust channel-wise illumination estimation"

This repository provides the official implementation of "Robust channel-wise illumination estimation." accepted in BMVC (2021).

Firas Laakom 4 Nov 08, 2022
This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong Poisons

Adversarial poison generation and evaluation. This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong

31 Nov 01, 2022
Modifications of the official PyTorch implementation of StyleGAN3. Let's easily generate images and videos with StyleGAN2/2-ADA/3!

Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation of the NeurIPS 2021 paper Alias-Free Generative Adversarial Net

Diego Porres 185 Dec 24, 2022