An autoencoder is a specialized type of rede neural artificial that is designed to learn efficient representations of data, typically for the purpose of dimensionality reduction or feature learning. It does this by encoding the input data into a lower-dimensional space and then decoding it back to its original form.
Autoencoders consistem em dois componentes principais: o encoder and the decoder. The encoder processes the input and compresses it into a representação compacta, often referred to as the espaço latente or bottleneck. The decoder then takes this compressed representation and reconstructs the original input data from it. The goal is to make the reconstructed output as close to the original input as possible.
Autoencoders são treinados usando um método chamado aprendizado não supervisionado, where the model learns to minimize the difference between the input and the output. This difference is often quantified using a loss function, such as mean squared error.
Autoencoders têm uma variedade de aplicações, incluindo:
- compressão de dados: Reducing the size of data while preserving important features.
- Redução de ruído: Removing noise from data while retaining the underlying signal.
- Extração de recursos: Learning useful features from raw data that can be used for other tasks like classification or clustering.
- Detecção de anomalias: Identifying unusual patterns in data by comparing the reconstruction error.
Existem também várias variações de autoencoders, como autoencoders variacionais (VAEs) and autoencoders de denoising, each with unique characteristics and uses in the field of aprendizado de máquina.