What is Autoencoders
Neural network models that learn to compress data into a compact representation and restore it back.
Definition
An autoencoder consists of two parts: an encoder and a decoder. The encoder turns the input into a smaller latent representation, and the decoder attempts to reconstruct the original object. Such models are used for compression, noise removal, anomaly search and feature learning.
Example
An autoencoder is trained to reconstruct normal images of parts, and then a large reconstruction error is used as a sign of a defect.
Why it matters
The term is important for understanding how neural networks can learn from data structure without explicit class labels.
How it works
The model receives the input, compresses it into latent space, and reconstructs the output. During training, it minimizes the difference between the original and reconstructed data.
Where it is used
- anomaly search
- data compression
- cleaning images and signals
Limitations
The autoencoder may learn to copy too easily if the architecture is poorly configured. Complex problems require proper constraints and quality checks.