  Learning from noisy labels is an important and long-standing problem in
machine learning for real applications. One of the main research lines focuses
on learning a label corrector to purify potential noisy labels. However, these
methods typically rely on strict assumptions and are limited to certain types
of label noise. In this paper, we reformulate the label-noise problem from a
generative-model perspective, $\textit{i.e.}$, labels are generated by
gradually refining an initial random guess. This new perspective immediately
enables existing powerful diffusion models to seamlessly learn the stochastic
generative process. Once the generative uncertainty is modeled, we can perform
classification inference using maximum likelihood estimation of labels. To
mitigate the impact of noisy labels, we propose the
$\textbf{L}$abel-$\textbf{R}$etrieval-$\textbf{A}$ugmented (LRA) diffusion
model, which leverages neighbor consistency to effectively construct
pseudo-clean labels for diffusion training. Our model is flexible and general,
allowing easy incorporation of different types of conditional information,
$\textit{e.g.}$, use of pre-trained models, to further boost model performance.
Extensive experiments are conducted for evaluation. Our model achieves new
state-of-the-art (SOTA) results on all the standard real-world benchmark
datasets. Remarkably, by incorporating conditional information from the
powerful CLIP model, our method can boost the current SOTA accuracy by 10-20
absolute points in many cases.
