arXiv Machine Learning
Jun 4
Optimal Transport Flow Matching by Design
arXiv:2606. 04092v1 Announce Type: cross Abstract: Flow matching models learn to transport samples from a simple prior distribution to a complex data distribution.
Low-Pass Flow Matching
arXiv:2606. 02177v1 Announce Type: new Abstract: Flow Matching typically relies on white noise sources, a choice often misaligned with the power spectra of natural data, which tend to decay with frequency.
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Hugging Face Trending Papers
Aug 6
Energy-Guided Flow Matching
Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dimensional space. Standard flow matching interpolates noise toward a fixed clean-image endpoint, leaving the spectral evolution to be learned implicitly.
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 AI
Aug 12
Flow Straight to Reality: Perceptually Consistent Flow Matching for Efficient Image Restoration
arXiv:2608. 10544v1 Announce Type: cross Abstract: Image restoration is fundamentally constrained by the tradeoff between distortion and perception: minimizing pixel-wise error yields over-smoothed results, whereas optimizing for perceptual realism often introduces structural deviations.
arXiv Machine Learning
Jul 7
Reflected Schr\"odinger Bridge Matching
arXiv:2607. 03626v1 Announce Type: new Abstract: Recent advances in generative modeling have enabled the efficient computation of Schr\"odinger bridges (SB) in high-dimensional settings by leveraging partially simulation-free training methods inspired by flow matching.
arXiv Machine Learning
Jun 19
Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems
arXiv:2606. 19802v1 Announce Type: new Abstract: Image restoration faces a fundamental tradeoff: methods that minimize error produce blurry reconstructions, while those that maximize perceptual quality yield sharp but less faithful images.
arXiv AI
Aug 3
WaiT for the Signal: Simple Frequency-Aware Flow-Matching
arXiv:2607. 28760v1 Announce Type: cross Abstract: As image generation models scale to ever higher resolutions, global coherence, local detail, and texture fidelity become critical axes for generation quality.
arXiv AI
Jun 29
Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super Resolution
arXiv:2605. 23264v2 Announce Type: replace-cross Abstract: Generative priors in Image Super-Resolution (SR) often compromise faithful restoration, we attribute this limitation to a fundamental spectral misalignment between isotropic objectives and the intrinsic natural image manifold.
Hugging Face Trending Papers
Jun 17
SpectralDiT: Timestep-Conditioned Spectral Residual Correction for Flow-Matching DiTs
We propose SpectralDiT, a lightweight modification to flow-matching Diffusion Transformers that adds timestep-conditioned spectral correction to the MLP residual branch. The module decomposes each residual update into low- and high-frequency components on the patch-token grid, then learns a zero-initialized additive gate so the model initially matches the baseline DiT.
arXiv AI
Jun 16
Topological Flow Matching
arXiv:2606. 15897v1 Announce Type: cross Abstract: Flow matching is a powerful generative modeling framework, valued for its simplicity and strong empirical performance.
arXiv AI
Aug 11
SNR-Edit: Structure-Aware Noise Rectification for Inversion-Free Flow-Based Editing
arXiv:2601. 19180v2 Announce Type: replace-cross Abstract: Inversion-free image editing using flow-based generative models challenges the prevailing inversion-based pipelines.
arXiv Machine Learning
Jul 30
Flow Map Learning via Nongradient Vector Flow
arXiv:2607. 26398v1 Announce Type: new Abstract: Diffusion and flow-based models benefit from simple regression losses, but inference incurs significant overhead because sampling requires integration.