English(EN) Efficient Training with Foresight: Multi-Token Auxiliary Supervision for Autoregressive Image Generation

新的 MTAR 框架提高了自回归图像生成效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

研究人员推出了一种新颖的训练框架 Multi-Token Autoregressive (MTAR)，旨在提高自回归图像生成效率。MTAR 通过引入多令牌预测 (MTP) 以获得更强的监督，令牌级对比正则化 (TCR) 以提高表示可分离性，以及用于加速训练的语义丢弃 (SD) 来解决传统下一个令牌预测的局限性。这些组件仅在训练期间使用，不影响推理速度。在 ImageNet 上的实验表明，MTAR 在生成质量和训练效率之间取得了卓越的平衡，其 FID 分数更低，训练时间显著缩短，性能优于 LlamaGen。 AI

影响这个新的训练框架可能带来更高效、更高质量的图像生成模型。

排序理由该集群包含一篇详细介绍新图像生成方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍新图像生成方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Guo Niu, Xiongfei Yao, Teng Wang, Nannan Zhu · 2026-08-27 04:00

高效的远见训练：用于自回归图像生成的、多令牌辅助监督

arXiv:2608.25386v1 Announce Type: new Abstract: Autoregressive (AR) image generation has shown strong potential for scalable high-fidelity synthesis by modeling images as discrete token sequences. However, traditional next token prediction (NTP) continues to suffer from sparse an…

本文如何被排名

报道来源 [1]

高效的远见训练：用于自回归图像生成的、多令牌辅助监督

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