Hugging Face Trending Papers
NAGO: Noise-Aware Generative Operator via Flow Matching
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arXiv Machine Learning
Jun 5
Scale-Adaptive Generative Flows for Multiscale Scientific Data
arXiv:2509. 02971v2 Announce Type: replace-cross Abstract: Flow-based generative models can face numerical challenges on scientific data with multiscale Fourier spectra, often producing large errors at fine scales.
arXiv Machine Learning
Sep 17
Beyond Random Couplings: Contrastive Noise Alignment in Generative Flows
The paper introduces Contrastive Noise Alignment (CNA), a training-time method for generative flow models that dynamically aligns Gaussian noise with data samples using a cross-modal InfoNCE objective. By modeling noise as an interacting particle system and regularizing with angular entropy and radial norm penalties, CNA reduces arbitrary data-noise couplings and flow curvature. Empirical results show that CNA improves generation quality, lowering FID by over 50% for few-step pixel-space generation compared to standard rectified flow and outperforming optimal transport baselines by at least 24%.
Hugging Face Trending Papers
Sep 29
Improving Function Space Flow Matching with Kernel Optimal Transport
Generative models for function-valued data, such as time series and solutions of partial differential equations, must learn distributions over infinite-dimensional spaces. Functional Flow Matching (FF...
arXiv Machine Learning
2d ago
Energy-Guided Transport for Projection-Free Physics-Informed Flow Matching
arXiv:2611.06711v1 Announce Type: new Abstract: Deep generative models, particularly Flow Matching frameworks, have emerged as powerful tools for synthesizing complex physical systems. However, stand...
arXiv Statistics ML
Oct 1
Flow Matching under Noisy Latent Structure: Beyond Exact Low-Dimensional Support
arXiv:2609.38918v1 Announce Type: cross Abstract: Flow Matching (FM) learns a velocity field whose ODE transports a simple source distribution to a target law. Existing finite-sample theory largely t...
arXiv Machine Learning
Sep 30
Improving Function Space Flow Matching with Kernel Optimal Transport
arXiv:2609.38049v1 Announce Type: new Abstract: Generative models for function-valued data, such as time series and solutions of partial differential equations, must learn distributions over infinite...