[diyer22]

No, DDN and VQ-VAE are clearly different.

Similarities: - Both map data to a discrete latent space.

Differences: - VQ-VAE needs an external prior over code indices (e.g. PixelCNN or a hierarchical prior) to model distribution. DDN builds its own hierarchical discrete distribution and can even act as the prior for a VQ-VAE-like system. - DDN’s K outputs are features that change with the input; VQ-VAE’s codebook is a set of independent parameters (embeddings) that remain fixed regardless of the input. - VQ-VAE produces a 2-D grid of code indices; DDN yields a 1-D/tree-structured latent. - VQ-VAE needs Straight-Through Estimator. - DDN supports zero-shot conditional generation.

So I’d call them complementary rather than “80 % the same.” (See the paper’s “Connections to VQ-VAE.”)