✨Diversity Has Always Been There in Your Visual Autoregressive Models

📝 Summary:
To combat diversity collapse in Visual Autoregressive models, DiverseVAR modifies feature maps without retraining. This restores generative diversity while maintaining high synthesis quality.

🔹 Publication Date: Published on Nov 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17074
• PDF: https://arxiv.org/pdf/2511.17074

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

#VisualAI #GenerativeModels #ModelDiversity #MachineLearning #ComputerVision

✨ViPO: Visual Preference Optimization at Scale

📝 Summary:
ViPO scales visual preference optimization using Poly-DPO for noisy data and constructing ViPO, a large high-quality dataset. This dual approach yields superior performance, emphasizing that algorithmic adaptability and data quality are crucial.

🔹 Publication Date: Published on Apr 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.24953
• PDF: https://arxiv.org/pdf/2604.24953
• Project Page: https://liming-ai.github.io/ViPO
• Github: https://liming-ai.github.io/ViPO

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

#VisualAI #MachineLearning #DeepLearning #Optimization #DataScience