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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Jul 24·5 min read
Mean-to-Score Discrete Diffusion: Posterior-Mean Denoisers for Score Entropy
TL;DR
Mean-to-Score Discrete Diffusion (M2S) introduces a method for generating clean-token posteriors from noisy data, significantly improving generative performance metrics. The approach reduces test BPD from 3.173 to 3.129 and achieves a generative PPL of 143.3 at 128 steps.
✦ Why It Matters
Consider implementing M2S in your generative models to enhance performance and reliability.
Key Takeaways
- Implement M2S to improve generative model performance.
- Use the linear mapping technique for clean-token posteriors.
- Evaluate your models against the new BPD and PPL metrics.
- Explore the implications of Bayes realizability in your work.
How It Works
M2S predicts a clean-token posterior mean and transforms it into a score using a kernel-dependent linear map. This method ensures that the generated scores are valid and compatible with the underlying data distribution, addressing the issues of negative weights and invalid score vectors seen in SEDD.