✨Beyond Binary Preference: Aligning Diffusion Models to Fine-grained Criteria by Decoupling Attributes

📝 Summary:
Current diffusion model alignment struggles with complex, fine-grained human expertise due to simplified preferences. This paper proposes a framework with hierarchical criteria and Complex Preference Optimization CPO, maximizing positive and minimizing negative attributes to improve generation qu...

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04300
• PDF: https://arxiv.org/pdf/2601.04300

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#DiffusionModels #AIAlignment #MachineLearning #GenerativeAI #PreferenceLearning

✨Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization

📝 Summary:
Semi-DPO addresses label noise in multi-dimensional visual preference learning. It treats consistent data as clean and conflicting data as noisy, using iterative refinement via pseudo-labeling. This improves alignment with complex human preferences and achieves state-of-the-art results.

🔹 Publication Date: Published on Apr 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.24952
• PDF: https://arxiv.org/pdf/2604.24952
• Project Page: https://liming-ai.github.io/SemiDPO
• Github: https://liming-ai.github.io/SemiDPO

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For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#MachineLearning #SemiSupervisedLearning #DPO #NoisyData #PreferenceLearning