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English(EN) VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers

VQ-Transplant 框架支持为视觉标记器有效集成 VQ 模块

研究人员开发了 VQ-Transplant,一个旨在将新的向量量化 (VQ) 模块高效集成到预训练视觉标记器中的框架,而无需进行广泛的重新训练。该方法保留了现有的编码器-解码器参数,并采用简短的解码器适应策略,将训练成本降低高达 95%,同时保持接近最先进的重建保真度。VQ-Transplant 旨在通过实现新颖量化技术的高效实验,使 VQ 研究更易于访问。 AI

影响 降低了开发新颖 AI 量化技术的门槛,可能加速离散视觉标记化领域的研究。

排序理由 这是一篇详细介绍 AI 模型开发新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

VQ-Transplant 框架支持为视觉标记器有效集成 VQ 模块

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Tool
这是一篇详细介绍 AI 模型开发新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
paper, infra
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xianghong Fang, Yuan Yuan, Dehan Kong, Tim G. J. Rudner ·

    VQ-Transplant:高效VQ模块集成预训练视觉分词器

    arXiv:2607.19575v1 Announce Type: new Abstract: Vector Quantization (VQ) underpins modern discrete visual tokenization. However, training quantization modules for state-of-the-art VQ-based models requires significant computational resources which, in practice, all but prevents th…