Blind Denoising Diffusion Models Offer Theoretical Framework Without Explicit Noise Conditioning
Researchers have developed a theoretical framework for blind denoising diffusion models (BDDMs) that eliminate the need to explicitly pass noise amplitude information into neural networks during training and sampling. The approach relies on an assumption that data distributions have low intrinsic dimensionality relative to their ambient dimension, and includes a Bayesian method for estimating noise levels from single noisy samples. This work addresses long-standing practical challenges in diffusion model design by providing principled alternatives to ad hoc noise schedules and contrived noise embeddings.
A new preprint on arXiv presents a complete theoretical analysis of blind denoising diffusion models, a variant of the widely-used denoising diffusion models (DDMs) that removes explicit noise conditioning from the neural network architecture. Current DDM implementations require practitioners to manually design noise embeddings and sampling schedules—choices that lack principled justification. The proposed BDDMs eliminate these design choices by not passing noise amplitude information into the network, instead relying on the network to implicitly learn noise levels. The theoretical justification depends on an assumption that the underlying data distribution has low intrinsic dimensionality relative to the ambient dimension. The authors introduce a Bayesian framework for estimating noise levels from individual noisy samples and propose an adaptive scheme validated by their analysis. Empirical comparisons demonstrate performance benefits of the adaptive BDDM approach relative to standard DDMs.
What's missing
The paper does not discuss computational complexity or runtime comparisons between BDDMs and standard DDMs. Additionally, the scope and limitations of the low intrinsic dimensionality assumption—specifically which real-world data distributions satisfy this assumption and which do not—are not detailed in the abstract.
What different sources said
- arXiv cs.LGCenter
Blind denoising diffusion models and the blessings of dimensionality
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