Summary of Discrete Distribution Networks, by Lei Yang
Discrete Distribution Networks
by Lei Yang
First submitted to arxiv on: 29 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Discrete Distribution Networks (DDN) model approximates data distribution using hierarchical discrete distributions. By generating multiple samples simultaneously, DDN represents distributions more effectively than traditional single-output models. The approach fits target distributions, including continuous ones, by selecting the output closest to Ground Truth (GT). This selected output is then fed back into the network as a condition for the next layer, generating new outputs more similar to GT. As the number of layers increases, the representational space expands exponentially, and generated samples become increasingly similar to GT. DDN demonstrates unique properties through experiments on CIFAR-10 and FFHQ. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DDN is a new way to understand data distribution. Instead of making one guess, it makes many guesses at once! This helps DDN understand complex patterns in the data better than other models. The model works by looking at what’s closest to the real answer (Ground Truth) and using that as input for the next step. It keeps doing this until it gets really good at generating answers that are close to the real thing. This is helpful because it lets us generate new examples that are similar to the ones we already have. |
