DDN: Discrete Distribution Networks
🥳 Accepted by ICLR 2025
🚀 The code has been released
Contributions of this paper:
We introduce a novel generative model , termed Discrete Distribution Networks (DDN), which demonstrates a more straightforward and streamlined principle and form.
, termed Discrete Distribution Networks (DDN), which demonstrates a more straightforward and streamlined principle and form. For training the DDN, we propose the Split-and-Prune optimization algorithm , and a range of practical techniques.
, and a range of practical techniques. We conduct preliminary experiments and analysis on the DDN, showcasing its intriguing properties and capabilities, such as zero-shot conditional generation without gradient and distinctive 1D discrete representations.
Left: Illustrates the process of image reconstruction and latent acquisition in DDN. Each layer of DDN outputs K distinct images, here K = 3 , to approximate the distribution P ( X ) . The sampler then selects the image most similar to the target from these and feeds it into the next DDN layer. As the number of layers increases, the generated images become increasingly similar to the target. For generation tasks, the sampler is simply replaced with a random choice operation.
Right: Shows the tree-structured representation space of DDN's latent variables. Each sample can be mapped to a leaf node on this tree.
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