Unverified60% confidenceFactExact time
The sampling operation itself is not differentiable, but during training the loss function is a regression on noise prediction, so gradients can be backpropagated normally.
2
Sources
60%
Confidence
Long-term
Relevance
10/26/2026
First Seen
Sources
Diffusion Models for Beginners: From DDPM Principles to UNet in Practice
bilibili唐宇迪的AI课堂10/9/2026
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Related Claims
VerifiedAfter geometric transformation, the transformation parameters must be recorded and the predicted boxes must be inverse-transformed to align them; ignoring this step will cause semantic misalignment in the consistency loss and harm training effectiveness79% similarUnverifiedALD uses a marginal preferential decomposition, expressing stable noise in the form of a scalar Gaussian scale mixture, so that the training objective returns to the standard Gaussian noise prediction loss, requiring only one additional scalar scale input.76% similarUnverifiedExperimental conclusion two: some self-distillation methods give attractive scores at the end of training, but during training they exhibit severe performance fluctuations or even collapse followed by recovery, and these problems cannot be found by looking only at the final metric.74% similarUnverifiedIn the sampled-gradient setting, when the learning rate η decreases, the expected gain under fixed β vanishes faster than the fluctuation itself, causing the benefit of momentum to be swallowed by noise.73% similarUnverifiedThe unified noise schedule leaves asymmetric workflows (synthesizing or editing one modality conditioned on the other) uncovered in training, so the model cannot handle them.73% similar
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