arXiv AI
NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training
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Noise in Diffusion Models Is a Learnable Input
The paper argues that the concrete random noise used in diffusion models is not merely a passive perturbation but a learnable input that can be exploited by the model. By analyzing how clean data and realized noise jointly form the noisy input, the authors show that the model can learn regularities in the data or in the noise structure, and that these two routes can interact. Experiments on MNIST and CIFAR‑10 using pseudorandom streams demonstrate that structured‑noise training can reduce prediction loss, but this advantage disappears when test noise is replaced with IID noise, indicating that the learned dependence is tied to the specific noise structure.
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From Atoms to Entropy: Optimal Noise Allocation for Diffusion Training in the Convex Regime
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Same Loss, Same Noise, Opposite Schedules: Noise Structure and Optimizer Normalization Jointly Determine Whether Learning-Rate Cooldown Helps
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arXiv AI
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MixFlow Training: Alleviating Exposure Bias with Slowed Interpolation Mixture
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The Emergence of Reproducibility and Generalizability in Diffusion Models
arXiv:2310. 05264v5 Announce Type: replace Abstract: In this work, we investigate an intriguing and prevalent phenomenon of diffusion models which we term as "consistent model reproducibility": given the same starting noise input and a deterministic sampler, different diffusion models often yield remarkably similar outputs.
arXiv AI
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Paradoxical noise preference in RNNs
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