I invented a new generative model and got accepted to ICLR
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…
In plain words
Discrete Distribution Networks is a generative model that produces multiple outputs simultaneously in a single forward pass to approximate data distributions, offering a fundamentally different approach from diffusion, GAN, VAE, and autoregressive models. The method uses these multiple outputs to represent a discrete distribution. It is designed for researchers and machine learning practitioners working on generative tasks and supports capabilities like zero-shot conditional generation. The research has been accepted to ICLR 2025.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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.
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