Codebook collapse is a problem that arises when training generative machine learning models that generate outputs using a fixed-length codebook, such as the Vector-Quantized Variational Autoencoder (VQ-VAE).

In ideal scenarios, the model’s fixed-size codebook is large enough to create a diverse set of output values. Codebook collapse happens when the model only learns to use a few of the values in the codebook–artificially limiting the diversity of outputs that the model can generate.

Codebook collapse is analogous to mode collapse, another problem commonly faced when training generative models.