SIMPLIFYING VQ-VAE

Summary from the TLDR AI newsletter

VectorQuantized-VAEs are typically the state of the art when trying to learn a specific discrete representation (e.g., tokens or codes). However, they are usually complex and fragile. This new paper proposes a simple quantization scheme that eliminates codebook collapse and complicated machinery such as commitment losses, codebook reseeding, code splitting, entropy penalties, etc.