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.
Autoencoder in a nutshell
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
- Dimensionality reduction
- Feature extractor
- Denoising images
- Image recognition and semantic segmentation
