RegCache algorithm improves vision encoder quantization by mitigating outliers

Researchers have developed a novel training-free algorithm called RegCache to address outlier issues in quantized vision encoders. This method introduces prefix tokens to mitigate outliers, improving performance particularly in low-bit quantization scenarios. RegCache is designed as a plug-in module that can be integrated with existing quantization techniques. AI

IMPACT This method could reduce the inference cost of vision encoders, enabling more efficient on-device processing and vision-language models.

RANK_REASON The cluster contains a research paper detailing a new algorithm for improving vision encoder quantization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• Connected Papers
• CORE Recommender
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv Recommender
• Influence Flower
• Litmaps
• RegCache
• ScienceCast
• scite Smart Citations
• Seunghyeon Kim

• paper
• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →