Autoencoders are neural networks designed to learn efficient representations of data through unsupervised learning.
– An encoder that compresses data – A bottleneck layer representing latent space – A decoder that reconstructs the original input
The encoder transforms input into a lower-dimensional encoding. The decoder then reconstructs the original data from this compressed representation, aiming for minimal loss.
– Dimensionality reduction – Image denoising – Anomaly detection – Generating synthetic data in various fields
– Denoising Autoencoders – Variationally Autoencoders (VAEs) – Sparse Autoencoders
– Reduces data dimensionality while preserving essential features – Enabling efficient storage – Faster processing for machine learning applications
Autoencoders play a crucial role in deep learning by enabling effective data compression and reconstruction, enhancing performance in unsupervised learning tasks.