I invented Discrete Distribution Networks, a novel generative model with simple principles and unique properties, and the paper has been accepted to ICLR2025!

Modeling data distribution is challenging; DDN adopts a simple yet fundamentally different approach compared to mainstream generative models (Diffusion, GAN, VAE, autoregressive model):

1. The model generates multiple outputs simultaneously in a single forward pass, rather than just one output. 2. It uses these multiple outputs to approximate the target distribution of the training data. 3. These outputs together represent a discrete distribution. This is why we named it "Discrete Distribution Networks".

Every generative model has its unique properties, and DDN is no exception. Here, we highlight three characteristics of DDN:

- Zero-Shot Conditional Generation (ZSCG). - One-dimensional discrete latent representation organized in a tree structure. - Fully end-to-end differentiable.

Reviews from ICLR:

> I find the method novel and elegant. The novelty is very strong, and this should not be overlooked. This is a whole new method, very different from any of the existing generative models. > This is a very good paper that can open a door to new directions in generative modeling.