扩散模型中置信度的局限性研究
Limits of Confidence in Diffusion
研究指出，离散扩散模型在通过多位置分布采样生成序列时存在局限，仅当各位置在给定已固定 token 条件下相互独立时，采样步才能匹配训练分布。实验在 ScanAndAdd 合成任务中验证了置信度排序会产生依赖性，导致生成分布的采样噪声基准总变差达到 29×，而单样本指标为 1.0。该发现揭示了基于位置分布的采样方法在处理具有内在依赖性的数据域时难以准确建模。
AuthorsRuss Webb, Amitis Shidani, Alice Bizeul, Dan Busbridge
Discrete diffusion, including remasking and uniform-state samplers, generate a sequence by writing multiple token positions per step, drawing each from a per-position distribution and choosing which positions to write from those same distributions. For domains of general interest (pixels, phonemes, or words) there are inherent dependencies between tokens. We show that a step matches the training distribution only when the positions it writes are conditionally independent given the tokens already fixed, that no product of per-position distributions can match a dependent group, and that per-position distributions do not determine whether a group is dependent: two joint distributions can have identical per-position marginals while differing in which combinations of values occur. On ScanAndAdd, a synthetic task whose joint distribution is available in closed form, we verify that every group of two or more undetermined positions a confidence ranking writes is dependent, and measure the generated distribution to be 29× the sampling-noise floor total variation while per-sample metrics are 1.0.
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来源：Apple Machine Learning Research · machinelearning.apple.com