An autoencoder is an unsupervised neural network architecture designed to learn efficient representations of input data by compressing it into a lower-dimensional latent space and then reconstructing the original data from this compressed form. This process enables the model to capture essential features and patterns within the data. Autoencoders consist of two main components:
- Encoder: Transforms the input data into a compressed latent representation.
- Decoder: Reconstructs the original data from the latent representation.
Autoencoders are widely used for tasks such as dimensionality reduction, feature extraction, and anomaly detection. Variants like denoising autoencoders, which reconstruct data from noisy inputs, and variational autoencoders, which model the distribution of data for generative purposes, have expanded their applications. For instance, in image processing, autoencoders can learn to compress images into a compact form and then reconstruct them, effectively capturing the most important features of the images.
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