Vector-Quantized Autoencoder

A Vector-Quantized Autoencoder learns latent representations through discrete code vectors rather than a continuous latent space. This provides a powerful way to represent visual or audio data with language-like discrete structure. VQ-based architectures have been especially effective in generative modeling and high-quality synthesis systems. The discrete latent space offers a different structural interpretation of representation learning.