arXiv Machine Learning
Aug 21
Continuous Adversarial MeanFlow Transfer
arXiv:2608. 19540v1 Announce Type: new Abstract: Training fast generators on new domains with limited data remains challenging for two reasons.
Continuous Adversarial MeanFlow for Few-Step Generative Adaptation from Any Parameterization
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Hugging Face Trending Papers
Aug 20
Continuous Adversarial MeanFlow Transfer
Training fast generators on new domains with limited data remains challenging for two reasons. First, adapting a pretrained diffusion or flow model to a new domain leaves its costly multi-step sampling unaddressed, and existing acceleration methods are tied to the source parameterization--$ε$, $x$, $v$, or $u$--leaving heterogeneous pretrained models with no common acceleration target.
arXiv AI
2d ago
NextGAN: Towards Simple and Scalable GAN Training for Image and Video Generation
NextGAN introduces a new training approach for GANs that regresses the critic’s input gradient onto displacements between paired reference and generated features in frozen representation spaces, effectively adding a gradient‑energy penalty while keeping the generator’s standard update. This method scales to very large pre‑trained image and video generators (up to 14 B parameters) without extra losses or teacher signals, achieving state‑of‑the‑art one‑step FIDs on ImageNet and superior VBench scores for text‑to‑video generation with a single 1.3 B model.
arXiv Machine Learning
Aug 12
Deciding When to Switch: E-Processes for Adaptive Minimax Training for Generative Adversarial Nets
arXiv:2608. 10096v1 Announce Type: cross Abstract: Modern data science increasingly gives rise to hypothesis-testing problems that are not naturally formulated in terms of parameters within prespecified statistical models.
arXiv Computer Vision
Oct 2
Smoother Flow Matching via Contrastive Trajectory Repulsion
arXiv:2610.01408v1 Announce Type: new Abstract: Trajectory crossing remains a critical bottleneck in Flow Matching (FM), and previous works typically view these crossings from a theoretical optimizat...
arXiv Machine Learning
Aug 27
Continuous Adversarial Flow Models
The paper introduces continuous adversarial flow models, a continuous-time flow framework trained with an adversarial objective that replaces the fixed mean-squared-error criterion of flow matching. By incorporating a learned discriminator, the method guides training toward a different generalized distribution, yielding samples more closely aligned with the target data distribution. Applied as a post‑training step, it markedly improves ImageNet 256px generation metrics—reducing the guidance‑free FID of latent‑space SiT from 8.26 to 3.63 and of pixel‑space JiT from 7.17 to 3.57—and also enhances guided generation and text‑to‑image benchmarks.
arXiv Computer Vision
2d ago
VA-TDM: A Unified View for Improving Few-Step Diffusion Models by Velocity Adversarial Distillation
arXiv:2611.06796v1 Announce Type: new Abstract: Few-step distillation offers efficient generation with diffusion and flow models, but preserving quality with only a few sampling steps remains challen...
arXiv Machine Learning
Jul 17
MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators
arXiv:2607. 15273v1 Announce Type: cross Abstract: MeanFlow generators achieve fast few-step sampling by predicting average velocities over time intervals, making them attractive for efficient generation.
arXiv Machine Learning
Sep 22
When Does Adversarial Refinement Help? A Negative Result and Open Problem in Adapting R3GAN to Time Series Imputation
The paper investigates whether the stable GAN architecture R3GAN can improve time‑series imputation when adapted to 1‑D temporal data. Using a coarse‑to‑fine refinement framework and a frequency‑domain discriminator, the authors evaluate 14 saved configurations across three datasets and find a negative result: most configurations either show negligible improvement or degrade performance compared to baseline methods. The study highlights that the usual argument—GANs optimize distributional objectives rather than point‑wise ones—does not fully explain the lack of benefit, and it poses an open problem regarding why a learned discriminator fails to provide useful refinement gradients while diffusion denoisers succeed, offering practical guidance on when adversarial refinement may be worthwhile.
arXiv AI
Jul 14
MixFlow Training: Alleviating Exposure Bias with Slowed Interpolation Mixture
arXiv:2512. 19311v2 Announce Type: replace-cross Abstract: This paper studies the training-testing discrepancy (a.
arXiv Computer Vision
Oct 15
SnapFlow: One-Step Action Generation for Flow-Matching VLAs via Progressive Self-Distillation
arXiv:2604.05656v2 Announce Type: replace Abstract: Vision-language-action (VLA) models with flow-matching action heads, such as {\pi}0 and {\pi}0.5, have become a standard recipe for generalist robo...
arXiv Computer Vision
1d ago
Expanding the Quality-Coverage Frontier with Competitive Flow Matching
The paper introduces a winner‑take‑all training strategy for flow‑matching generators that injects a random low‑dimensional conditioning code to create multiple candidate velocity predictions. By applying the loss only to the best‑matching candidate, the method allows distinct modes to shape separate predictions, mitigating the quality‑coverage tradeoff inherent in traditional flow matching. The approach also enables a new auxiliary conditioning‑scaling guidance, and demonstrates significant improvements on ImageNet‑256, reducing Fréchet Distance scores by more than 70% on both SiT and RAEv2 testbeds.