Image Anomaly Detection using Autoencoders

Explore Deep Convolutional Autoencoders to identify Anomalies in Images.

This article is an experimental work to check if Deep Convolutional Autoencoders could be used for image anomaly detection on MNIST and Fashion MNIST.

Functionality: Autoencoders encode the input to identify important latent feature representation. It then decodes the latent features to reconstruct output values identical to the input values.

Objective: Autoencoder’s objective is to minimize reconstruction error between the input and output. This helps autoencoders to learn important features present in the data.

Architecture: Autoencoders consists of an Encoder network and a Decoder network. The encoder encodes the high dimension input into a lower-dimensional latent representation also referred to as the bottleneck layer. The decoder takes this lower-dimensional latent representation and reconstructs the original input.

Usage: Autoencoder are used for

Here we will use Tensorflow 2.3 and Fashion MNIST dataset.

To identify image anomalies, we will use the below architecture.

The encoder will compress the input data to its latent representation. The decoder will decompress the encoded latent representation to reconstruct the input data. The loss between the original input and the reconstructed input will be measured using the SSIM loss function. If the input belongs to images from the trained dataset then reconstruction loss will be smaller and if there is an anomaly then the reconstruction loss will be high.

SSIM is Structural Similarity Index Measure which is used to measure the similarity between two images. The SSIM value is between -1 and 1.

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