How did this get accepted without any baseline comparisons? They should have compared this to VQ-VAE, diffusion inpainting and a lot more.
replies(1):
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.
> Many high rated papers would have been done by someone else if their authors never published them or were rejected. However, if this paper is not published, it is not likely that anyone would come up with this approach. This is real publication value. I am reminding again the original diffusion paper from 2015 (Sohl-Dickstein) that was almost not noticed for 5 years. Had it not been published, would we have had the amazing generative models we have today?
Cite from: https://openreview.net/forum?id=xNsIfzlefG¬eId=Dl4bXmujh1
Besides, we compared DDN with other approaches in the Table 1 of original paper, including VQ-VAE.