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Anomaly detection using Variational Autoencoder(VAE)

version 1.0.0 (16.1 MB) by Takuji Fukumoto
You can learn how to detect and localize anomalies on image using Variational Autoencoder


Updated 07 Nov 2019

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On shipping inspection for chemical materials, clothing, and food materials, etc, it is necessary to detect defects and impurities in normal products.
In the following link, I shared codes to detect and localize anomalies using CAE with only images for training.

In this demo, you can learn how to apply Variational Autoencoder(VAE) to this task instead of CAE.
VAEs use a probability distribution on the latent space, and sample from this distribution to generate new data.

このデモでは代わりにVariational Autoencoderを適用した

■Anomaly detection and localization using deep learning(CAE)

[Keyward] 画像処理・ディープラーニング・DeepLearning・IPCVデモ ・異常検出・外観検査・オートエンコーダー・サンプルコード・変分オートエンコーダ

■Auto-Encoding Variational Bayes [2013]
Diederik P Kingma, Max Welling

Cite As

Takuji Fukumoto (2020). Anomaly detection using Variational Autoencoder(VAE) (, MATLAB Central File Exchange. Retrieved .

Comments and Ratings (2)


Kei Otsuka

MATLAB Release Compatibility
Created with R2019b
Compatible with R2019b and later releases
Platform Compatibility
Windows macOS Linux

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