Variational Autoencoder
A generative model that learns a compressed, lower-dimensional representation (latent space) of input data and can generate new data by sampling from this learned space.
Why It Matters
VAEs enable data generation, anomaly detection, and learning meaningful data representations. They are foundational to understanding modern generative AI.
Example
A VAE trained on faces learning a smooth latent space where you can interpolate between two faces, gradually morphing one into the other.
Think of it like...
Like a zip file for data — it compresses information into a compact code, and you can create new variations by tweaking that code slightly.
Related Terms
Autoencoder
A neural network that learns to compress data into a lower-dimensional representation (encoding) and then reconstruct it back (decoding). It learns what features are most important for faithful reconstruction.
Latent Space
A compressed, lower-dimensional representation of data learned by a model. Points in latent space capture the essential features of the data, and nearby points represent similar data items.
Generative AI
AI systems that can create new content — text, images, music, code, video — rather than just analyzing or classifying existing data. These models learn patterns from training data and generate novel outputs that resemble the original data.
Generative Adversarial Network
A framework where two neural networks compete — a generator creates fake data and a discriminator tries to tell real from fake. This adversarial process drives both networks to improve, producing increasingly realistic outputs.
Representation Learning
The process of automatically discovering useful features or representations from raw data, rather than manually engineering them. Deep learning excels at learning hierarchical representations.