What Is an Autoencoder? How It Works, Types, and Real-World Uses

Key takeaways

• An autoencoder is a deep learning network that uses unsupervised machine learning techniques to identify key features within data.

• Types of autoencoders include variational autoencoders, sparse autoencoders, convolutional autoencoders, denoising autoencoders, and contractive autoencoders.

• Autoencoders are used for anomaly detection, to identify fraud, and for generative modeling, to produce images, text, or audio.

Autoencoders work by producing outputs that replicate the input data in a compressed form, enabling more efficient storage and processing. Learn more about autoencoders and the different ways you can use them in machine learning.

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What is an autoencoder in deep learning?

An autoencoder is a deep learning algorithm that compresses its input data and learns to replicate it, producing a reconstructed version. For example, an autoencoder can compress an image or video and then reconstruct it, decreasing the amount of storage required and increasing the speed at which multimedia is transmitted.

Autoencoders use unsupervised learning to train the model to develop accurate and efficient representations of data. By striving to replicate the input data as closely as possible, the autoencoder enables the model to identify useful features for applications such as anomaly detection, feature learning, and efficient data processing and storage [1].

Is an autoencoder a CNN?

An autoencoder isn’t a convolutional neural network (CNN). Rather, it's a specific architecture you can use in various neural networks, including CNNs and recurrent neural networks (RNNs) [1].

How do autoencoders work?

Autoencoders consist of two primary architectural components, an encoder and a decoder.

1) Encoder compresses input to latent space

The encoder takes the input data and compresses it into a representation called the latent space using dimensionality reduction, which is how the autoencoder learns features from the data. The latent space representation is then forwarded to the decoder.

2) Decoder reconstructs

The decoder in a deep learning neural network contains hidden layers with nodes that decompress the encoded or compressed version of data to reconstruct it in a form that closely resembles the pre-encoded data.

3) Loss function measures reconstruction error

The difference between the original input and reconstructed output is known as the reconstruction error. This error is quantified by a loss function during training.

4) Backpropagation minimizes it.

Backpropagation helps minimize the reconstruction error.

Why are autoencoders useful?'

Autoencoders are useful largely thanks to their ability to reduce dimensionality, minimizing the number of features in the data while preserving the most important information. This key characteristic of autoencoders makes data processing far more efficient for machine learning applications. Other benefits of using autoencoders include [2]:

• Noise reduction: Autoencoders can filter out unnecessary data, reducing noise in images and improving the quality of the input data.

• Anomaly detection: While an encoder is in the training process, it can detect unusual patterns and outliers to help manage quality control and identify fraud.

• Feature learning: An encoder can identify key features in the data, which help the decoder reconstruct nearly accurate representations of the data and support machine learning tasks such as regression.

• Generative modeling: Generative models can use autoencoders to help generate new data, including text, image, and audio data.

What are the types of autoencoders?

A few common types of autoencoders include variational autoencoders, convolutional autoencoders, denoising autoencoders, and LSTM autoencoders. Explore the different ways you can use each [3]:

• Variational autoencoder (VAE): A VAE is a generative model that performs both compression and synthesis. By following a probabilistic approach, VAEs excel at generating new data that reflects the input data. VAEs are often used for image generation, but other uses include generating new drug molecules and detecting anomalies.

• Convolutional autoencoder (CAE): By using convolutional layers in the neural network, convolutional autoencoders can perform unsupervised learning to classify and reconstruct data. CAEs are useful for image denoising, anomaly detection, and feature extraction, among other uses.

• Denoising autoencoder (DAE): Denoising autoencoders are trained with noise purposefully added to the input data. They learn to filter the partially corrupted data, often audio or image data, and remove the noise when reconstructing it back to its original form.

• Long short-term memory (LSTM) autoencoder: The LSTM autoencoder can remember data points throughout a long sequence, such as time-series data, enabling it to learn patterns so it knows what "normal" is, and then identify anomalies for use cases such as stock prices, weather patterns, and disease progression [4].

• Undercomplete autoencoder (undercomplete AE): You can use undercomplete autoencoders to prevent overfitting to the training data. This is the simplest autoencoder structure, prioritizing the learning of key features for reconstructing the input data.

• Sparse autoencoder (SAE): A sparse autoencoder limits how many nodes can activate at the same time within the hidden layers and assigns nodes to learn specific features, helping to prevent overfitting while also increasing learning capacity.

• Contractive autoencoder (CAE): With its ability to work through slight variations in input data, contractive autoencoders can manage noise to still develop accurate reconstructions for use cases such as feature learning, denoising, and data generation.

• Concrete autoencoder (CAE): Capable of transforming discrete variables into continuous variables, concrete autoencoders consist of a specialized layer, called the concrete layer, where the nodes select features from data, following a specific assignment.

Read more: What Is an LSTM Neural Network?

Is an autoencoder better than PCA?

An autoencoder does not, by default, outperform principal component analysis (PCA). Autoencoders and PCA are both useful techniques in unsupervised learning for dimensionality reduction, but each presents unique advantages depending on the use case. For example, autoencoders are more suitable when the data features have non-linear connections, while PCA is optimal for linear transformation [5].

What is an autoencoder used for? Real-world applications

Because autoencoders work with a wide range of data types, including text, images, videos, and audio, their use cases span many industries. For example, video streaming companies use autoencoders to compress videos to reduce storage requirements, as well as speed up video transmission. Another area where autoencoders are making an impact is in fraud detection, as their anomaly detection capabilities can help spot abnormal patterns.

The ability of autoencoders to denoise data makes them especially useful in applications using image data. For example, autoencoders can reconstruct low-quality images or even add color to black-and-white images. In the health care industry, autoencoders are valuable tools for medical image analysis, as they’re capable of effectively analyzing MRI images and identifying disease markers.

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