Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints
Learn to unlearn undesirable data in diffusion models using a unified framework with KL divergence and likelihood constraints, improving model utility and data privacy
- Formulate unlearning as a constrained optimization problem using KL divergence and likelihood constraints
- Define the objective function to minimize deviation from a pretrained model
- Apply separation constraints from unlearning distributions to ensure data privacy
- Implement the unified framework using popular deep learning libraries like PyTorch or TensorFlow
- Evaluate the performance of the unlearning framework using metrics like accuracy and F1-score
ML researchers and engineers working with diffusion models can benefit from this framework to remove unwanted data and concepts while preserving model performance, and data scientists can apply this technique to improve data privacy and model reliability
💡 Unlearning in diffusion models can be formulated as a constrained optimization problem, enabling the removal of unwanted data while preserving model performance
🚀 Unlearn undesirable data in diffusion models with a unified framework! 🤖 Improve model utility and data privacy with KL divergence and likelihood constraints #ML #DiffusionModels
Key Takeaways
Learn to unlearn undesirable data in diffusion models using a unified framework with KL divergence and likelihood constraints, improving model utility and data privacy
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Abstract:
arXiv:2605.30825v1 Announce Type: cross Abstract: Unlearning in diffusion models aims to remove undesirable data or concepts while preserving the utility of pretrained models -- two fundamentally conflicting objectives. We propose a principled constrained optimization framework that formulates unlearning as minimizing the deviation from a pretrained model, subject to explicit separation constraints from the unlearning distributions. Specifically, we formulate three constrained optimization probl
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