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cs.LG2026
Tracing Generated Samples to Training-Data Clusters in Flow-Matching Models
Rania Briq, Ohad Fried, Michael Kamp +1
Understanding which training samples influence a generated image is an important problem in generative modeling. In flow matching, training samples influence the generated image th…
cs.LG2026
Exploring and Exploiting Stability in Latent Flow Matching
Rania Briq, Michael Kamp, Ohad Fried +2
In this work, we show that Latent Flow-Matching (LFM) models are robust to different types of perturbations, including data reduction and model capacity shrinkage. We characterize…
cs.LG2024★ 1 cited
Data Pruning in Generative Diffusion Models
Rania Briq, Jiangtao Wang, Stefan Kesselheim
Data pruning is the problem of identifying a core subset that is most beneficial to training and discarding the remainder. While pruning strategies are well studied for discriminat…