Can you train this model to detect objects (e.g detect a fish in the picture)?
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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.
Much like DiffusionDet, which applies diffusion models to detection, DDN can adopt the same philosophy. I expect DDN to offer several advantages over diffusion-based approaches: - Single forward pass to obtain results, no iterative denoising required. - If multiple samples are needed (e.g., for uncertainty estimation), DDN can directly produce multiple outputs in one forward pass. - Easy to impose constraints during generation due to DDN's Zero-Shot Conditional Generation capability. - DDN supports more efficient end-to-end optimization, thus more suitable for integration with discriminative models and reinforcement learning.