← 研踪 PaperTrailarXiv:2611.16306 · v1
Overcoming Spurious Correlations in Conditional Diffusion Models via Doubly Robust Target Correction
首次提交 2026-11-12 13:07
版本更新 2026-11-12 13:07
雷达观测 2026-11-13 14:16
摘要
While conditional diffusion models have achieved remarkable success in generation tasks, they memorize spurious correlations within training data. This causes them to deviate from ideal target distributions. Minimizing a covariate shift-adjusted target risk can address this issue. However, retraining models from scratch for different distributions is computationally prohibitive. To overcome this limitation, we propose DR-Diff, a framework that efficiently fine-tunes pre-trained conditional diffusion models. Specifically, DR-Diff leverages a functional gradient approach to derive a pseudo-target. This pseudo-target captures the residual between the base model's noise prediction and the ground-truth noise under the target distribution. Training the model to fit this pseudo-target enables efficient target adaptation. Theoretically, we prove that DR-Diff guarantees target risk reduction at the population level. It also achieves a pseudo-doubly robust property, providing resilience against density ratio estimation errors and base model misspecifications. Empirically, DR-Diff matches the noise prediction accuracy of models trained from scratch on synthetic datasets. Furthermore, it mitigates inter-group disparities in downstream classification tasks.
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v1 · 2026-11-12 13:07 · 最新观测版本
Overcoming Spurious Correlations in Conditional Diffusion Models via Doubly Robust Target Correction
While conditional diffusion models have achieved remarkable success in generation tasks, they memorize spurious correlations within training data. This causes them to deviate from ideal target distributions. Minimizing a covariate shift-adjusted target risk can address this issue. However, retraining models from scratch for different distributions is computationally prohibitive. To overcome this limitation, we propose DR-Diff, a framework that efficiently fine-tunes pre-trained conditional diffusion models. Specifically, DR-Diff leverages a functional gradient approach to derive a pseudo-target. This pseudo-target captures the residual between the base model's noise prediction and the ground-truth noise under the target distribution. Training the model to fit this pseudo-target enables efficient target adaptation. Theoretically, we prove that DR-Diff guarantees target risk reduction at the population level. It also achieves a pseudo-doubly robust property, providing resilience against density ratio estimation errors and base model misspecifications. Empirically, DR-Diff matches the noise prediction accuracy of models trained from scratch on synthetic datasets. Furthermore, it mitigates inter-group disparities in downstream classification tasks.
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