Perform fraud detection using Autoencoders in TensorFlow

Learn what are AutoEncoders, how they work, their usage, and finally implement Autoencoders for anomaly detection.

AutoEncoder is a generative unsupervised deep learning algorithm used for reconstructing high-dimensional input data using a neural network with a narrow bottleneck layer in the middle which contains the latent representation of the input data.

Autoencoder consists of an Encoder and a Decoder.

• Encoder network: Accepts high-dimensional input data and translates it to latent low-dimensional data. The input size to an Encoder network is larger than its output size.
• Decoder network: The Decoder network receives the input from the Encoder coder’s output. Decoder’s objective is to reconstruct the input data. The output size of a Decoder network is larger than its input size.

The Autoencoder accepts high-dimensional input data, compress it down to the latent-space representation in the bottleneck hidden layer; the Decoder takes the latent representation of the data as an input to reconstruct the original input data.

Autoencoders Usage

• **Dimensionality Reduction. **The Encoder encodes the input into the hidden layer to reduce the dimensionality of linear and nonlinear data; hence it is more powerful than PCA.
• Recommendation Engines
• Anomaly Detection: Autoencoders tries to minimize the reconstruction error as part of its training. Anomalies are detected by checking the magnitude of the reconstruction loss.
• Denoising Images: An image that is corrupted can be restored to its original version.
• Image recognition: Stacked autoencoder are used for image recognition by learning the different features of an image.
• Image generation: Variational Autoencoder(VAE), a type of autoencoders, is used to generate images.

Read about different types of Autoencoder here.

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