跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+1030新增论文
+790其中 AI 相关
+128已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-12 01:48 · 全站最近入库：2026-11-11 22:47各信源进度 →

统计口径与数据新鲜度

统计 2026-11-11 02:43 — 2026-11-12 02:43（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-12 01:48最近发现新增版本：2026-11-11 22:47库内论文最新更新时间：2026-11-11 08:00

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

21,673 已收录论文18,321 AI 相关6,056 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 2 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-12 01:46（北京时间）
版本更新 · v8arXiv:2603.26783

Can We Change "Stroke Size" to Improve Flow Matching?

Yunwei Bai, Ying Kiat Tan, Tsuhan Chen

原文摘要

Deep-learning-based image generation models are useful but difficult to train. Inspired by the varying stroke sizes used in human painting, we hypothesize that applying ``rough strokes'' can attenuate prediction biases and improve flow-matching image generation. We develop MultiStroke, which progressively varies the spatial granularity of the clean-signal component in the velocity regression target. A lightweight corrector aligns adjacent roughness levels during sampling. On ImageNet-1K, MultiStroke reduces FID by $22.5\%$ relative to a matched flow-matching baseline, despite the limited corrector capacity. Code is available at https://github.com/DaChengRuoQue/MultiStroke-Code. A quick video explanation is available at https://dachengruoque.github.io/MultiStroke-Web/.

cs.CV · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.05407

PEEK: One-Step-Look-Ahead Exposure Bias Correction for Diffusion Sampling

Yunwei Bai, Ying Kiat Tan, Tsuhan Chen

原文摘要

Diffusion image generators owe much of their practical success to accurate denoising networks and strong numerical samplers. However, during sampling, the denoiser predicts the next step from flawed inputs that differ substantially from real data. This train-test gap, or exposure bias, caps image generation quality (i.e., blurry images, missing details, or distorted shapes). To mitigate the problem, we propose PEEK, a training-free deterministic sampler plugin. At each eligible denoising step, PEEK first performs a provisional one-step update, evaluates the denoiser once at the look-ahead state, and interpolates the current clean prediction with this one-step-later prediction to form the next noisy state. By taking the additional step, we obtain a generally more image like prediction from the higher-signal noisy state, mitigating exposure bias. To optimize the interpolation positions, PEEK searches for an interpolation schedule with a greedy metric-driven search made efficient by the PEEK prefix and baseline noise-schedule-rebasing caches. Across pixel-space and latent-space diffusion models with unconditional, label-guided, and text-guided settings on CIFAR-10, LSUNBedroom,I mageNet, and MS-COCO,the look-ahead correction consistently improves over baselines matched by number of function evaluations (NFE), with Fréchet Inception Distance (FID) reductions up to 49.3%. The one-time per-model calibration, the only extra cost, takes at most 2.2 minutes in our experiments. Project website is available at https://dachengruoque.github.io/PEEK-Video/.

cs.CV · cs.AI

查看论文
arXiv ↗PDF ↗