Training data attribution in diffusion models via mirrored unlearning and noise-consistent skew

📰 ArXiv cs.AI

Learn to attribute training data in diffusion models using mirrored unlearning and noise-consistent skew for improved reliability and robustness

advanced Published 19 May 2026

Action Steps

Implement mirrored unlearning to remove attribution signals from training data
Apply noise-consistent skew to diffusion models for robust attribution
Fine-tune diffusion models using the proposed MUCS approach
Evaluate the reliability and robustness of TDA using metrics such as attribution accuracy
Integrate MUCS into existing diffusion model pipelines for improved interpretability

Who Needs to Know This

Machine learning researchers and engineers working with diffusion models can benefit from this technique to improve model interpretability and reliability

Key Insight

💡 Mirrored unlearning and noise-consistent skew can enhance the reliability and robustness of training data attribution in diffusion models

Key Takeaways

Learn to attribute training data in diffusion models using mirrored unlearning and noise-consistent skew for improved reliability and robustness

Full Article

Title: Training data attribution in diffusion models via mirrored unlearning and noise-consistent skew

Abstract:
arXiv:2605.17938v1 Announce Type: cross Abstract: Training data attribution (TDA) should enable generative model interpretability and foster a variety of related downstream tasks. Nonetheless, current TDA approaches lack reliability and robustness, preventing their adoption in real-world setups. In this paper, we take a decisive step towards more reliable and robust TDA for diffusion models. We propose to perform TDA with mirrored unlearning and noise-consistent skew (MUCS). The idea is to fine-

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