The paper introduces Difficulty-Calibrated Flow Matching, a method that adapts the noise-to-data interpolation schedule in Conditional Flow Matching based on a pilot run’s loss profile. By setting the schedule to the quantile function of this difficulty profile, the training trajectory spends more time where the velocity is hardest to learn. Experiments on CIFAR-10, MNIST, and Fashion‑MNIST show that this calibrated path achieves the best FID on CIFAR‑10 and outperforms all fixed schedules in large‑batch, few‑update settings, where compute is most limited.
By Airin Akter Tania, Md Raihan Khan
RiskTraf introduces a risk-extrapolated residual learning approach for multi-variate traffic flow prediction, leveraging raw flow, speed, and occupancy data from the new PEMSB-3V benchmark. The method freezes a trained spatio-temporal backbone and adds a lightweight residual head that learns from historical speed and occupancy to correct flow predictions across different traffic regimes. Experiments show consistent improvements over various backbones and outperform existing debiasing and distribution-shift adaptation techniques.
By Guangyu Wang, Zhidan Liu
arXiv:2605. 27991v2 Announce Type: replace-cross Abstract: Gradient-flow optimization is usually viewed as an algorithmic procedure for minimizing empirical loss, with training duration selected by validation or heuristic early-stopping rules.
By Minhao Yao, Ruoyu Wang, Xihong Lin, Lin Liu, Zhonghua Liu
Policy‑Mask is a budget‑aware policy that jointly selects a timestep, a diffusion block, and a subset of channels from a frozen diffusion model to optimize downstream discriminative tasks. By normalizing channel scores and training on the downstream objective, it outperforms fixed‑extraction baselines on segmentation, classification, and semantic correspondence, achieving state‑of‑the‑art results on ADE20K, CIFAR‑100, and SPair‑71k. The method also extends to diffusion transformers by mapping U‑Net channels to image‑token hidden dimensions.
By Ivan Listopadov, Maksim Nakhodnov, Aibek Alanov
Classifier-free guidance (CFG) is the default mechanism for conditional generation in diffusion models, but the distribution sampled by its deterministic guided dynamics is not captured by the usual product-distribution heuristic $p_0^ωq_0^{1-ω}$. We analyze CFG through the probability flow ODE and derive exact analytic path-integral representations of the induced distributions for both constant and time-dependent guidance.
arXiv:2610.03503v1 Announce Type: new
Abstract: Training-free posterior sampling methods, also known as Plug-and-Play methods, leverage pretrained unconditional diffusion or flow-matching models to s...
By Liam Moroy, Jean-Fran\c{c}ois Giovannelli, Yoann Altmann, Steve McLaughlin, Fr\'ed\'eric Champagnat, Guillaume Bourmaud