INFO
A type of neural network designed to learn efficient representations of data by compressing input into a lower-dimensional form (encoding) and then reconstructing it back (decoding) as closely as possible to the original.
- Represent one of the foundational unsupervised deep learning architectures
Components:
- Compression (Encoder): Transforms input data into a compact latent representation
- Latent Space (Bottleneck): The compressed form that captures the most essential features
- Decompression (Decoder): Reconstructs the original data from the latent representation
How it Works
- uses Unsupervised Learning, minimizing a reconstruction loss between the input and output
Goal
- Dimensionality Reduction: distill complex, high-dimensional data into a reduced, meaningful representation or latent space
Reconstruction error
- Discrepancy between original data and decoded reconstruction
- When minimized will
- Uncover intrinsic features
- Enable businesses to process large datasets efficiently
- Variants enhances capability
- Denoising autoencoders: facilitating noise removal
- Variational autoencoders: probabilistic generation of new data