Suhang Yao, Xin Li, Xijun Wang, Yufei Wang, Yeying Jin, Zhibo Chen · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29959 · open-access PDF
基于变压器的模型在未知的压缩设置下在盲压图像恢复(CIR)上表现强劲,但其密集的投影操作员使部署成本昂贵。我们通过实验性、操作员级别的分析来研究此类模型的无培训加速。具体来说,我们对计算集中的地方进行分析,在压缩输入下描述频道式激活分布,并检查代表性投影重量的单值分解。
Machine translation (Meta M2M100); the English original follows.
Transformer-based models achieve strong performance on blind compressed image restoration (CIR) under unknown compression settings, but their densely stacked projection operators make deployment costly. We study training-free acceleration for such models through an empirical, operator-level analysis. Specifically, we profile where computation is concentrated, characterize channel-wise activation distributions under compressed inputs, and examine the singular-value decay of representative projection weights. These measurements identify the recurring projections in the attention, feed-forward, c
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.29959, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange