Definition
In machine learning, latent variables are unobserved factors that influence observed data. In neural networks, particularly autoencoders and diffusion models, latent spaces represent compressed, abstract embeddings of input data. These representations capture semantic meaning or structural properties, allowing models to manipulate data efficiently, interpolate between concepts, or generate new samples by navigating this continuous vector space.
Summary
Refers to hidden, underlying variables or representations within a model’s internal space that capture essential features of data.
Key Concepts
- Latent Space
- Embeddings
- Dimensionality Reduction
- Representation Learning
Use Cases
- Image Generation
- Anomaly Detection
- Data Compression