Anomaly detection using Variational Autoencoder(VAE)

You can learn how to detect and localize anomalies on image using Variational Autoencoder
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Aggiornato 25 dic 2020

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.

[Japanese]
正常な画像のみ使ってCAEモデルを学習させ,正常な画像に紛れる異常をディープラーニングを用いて検出ならびに位置の特定を行えるコードを下記のリンクで紹介しました。
このデモでは代わりにVariational Autoencoderを適用した
方法をご紹介します。
VAEは潜在変数に確率分布を使用し、この分布からサンプリングして新しいデータを生成するものです。

■Anomaly detection and localization using deep learning(CAE)
https://jp.mathworks.com/matlabcentral/fileexchange/72444-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
https://arxiv.org/pdf/1312.6114.pdf

Cita come

Takuji Fukumoto (2024). Anomaly detection using Variational Autoencoder(VAE) (https://github.com/mathworks/Anomaly-detection-using-Variational-Autoencoder-VAE-/releases/tag/1.0.1), GitHub. Recuperato .

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Compatibile con R2019b e release successive
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Versione Pubblicato Note della release
1.0.1

See release notes for this release on GitHub: https://github.com/mathworks/Anomaly-detection-using-Variational-Autoencoder-VAE-/releases/tag/1.0.1

1.0.0

Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.
Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.