Autoencoders are a kind of neural network that doesn’t need labeled data for training. The model learns a compressed version of the data to recreate the original with little loss of information.
Autoencoders consist of two main parts: an encoder and a decoder. The encoder shrinks the data and the decoder expands it back. The decoder takes the latent representation and maps it back to the original input space. In training, the encoder and decoder work together to fix errors between input and output.
Here’s how an autoencoder works:
- Encoding: The input data is fed into the encoder, which maps it to a lower-dimensional representation in the latent space.
- Decoding: The latent representation is fed into the decoder, which maps it back to the original input space.
- Training: The encoder and decoder work together to fix errors. They are trained with algorithms like gradient descent.
Autoencoders have several advantages, including:
- Unsupervised learning: Autoencoders do not require labeled data for training, which makes them useful for tasks where labeled data is scarce or expensive to obtain.
- Dimensionality reduction: Autoencoders help reduce the number of dimensions in data. They do this by learning a compressed version of the input data.
- Anomaly detection: You can use autoencoders to spot unusual data points. It works by comparing the error in reproducing new data to the error in reproducing known data in training.
- Generative modeling: Autoencoders can create new things by taking ideas from the data they were trained on. They do this by making a map of the training data and then using it to generate new things (like pictures or music).
Overall, autoencoders are a powerful tool for unsupervised learning, dimensionality reduction, anomaly detection, and generative modeling.