arXiv Machine Learning
Unifying Distributional Training for One-Step Visual Generation
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jul 30
Amortized Moment Matching for Visual Generation
arXiv:2607. 26860v1 Announce Type: new Abstract: We propose amortized moment matching, utilizing neural networks to learn data moments as distributional training signals.
arXiv Machine Learning
Sep 1
Reward-guided Fine-Tuning of One-Step Generative Models via Wasserstein Gradient Flow
arXiv:2608.29647v1 Announce Type: new Abstract: To mitigate the time complexity of generative models, one-step generative models have recently emerged through direct mapping from noise to data in a s...
arXiv AI
Jun 2
Better Source, Better Flow: Learning Condition-Dependent Source Distribution for Flow Matching
arXiv:2602. 05951v2 Announce Type: replace-cross Abstract: Flow matching has recently emerged as a promising alternative to diffusion-based generative models, particularly for text-to-image generation.
arXiv AI
Jun 16
Efficient Flow Matching using Latent Variables
arXiv:2505. 04486v4 Announce Type: replace-cross Abstract: Flow matching models have shown great potential in image generation tasks among probabilistic generative models.
arXiv AI
1d ago
Posterior sampling by source-space MCMC via prior-based few-step transport maps
arXiv:2610.01034v1 Announce Type: cross Abstract: Bayesian inference increasingly uses informative but implicit priors represented only by samples, such as historical ensembles, simulator outputs, an...
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.