Summary of Local Flow Matching Generative Models, by Chen Xu et al.
Local Flow Matching Generative Models
by Chen Xu, Xiuyuan Cheng, Yao Xie
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Local Flow Matching (LFM) framework is a modern approach to density estimation using flow-based generative models. Unlike existing methods, LFM employs a simulation-free scheme and incrementally learns a sequence of Flow Matching sub-models. Each sub-model matches a diffusion process over a small step size in the data-to-noise direction, reducing the gap between the two distributions. Theoretically, LFM guarantees generation based on the χ2-divergence between generated and true data distributions. Experimentally, LFM demonstrates improved training efficiency and competitive generative performance compared to FM on tabular data and image datasets. Its applicability is also shown in robotic manipulation policy learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Density estimation using flow-based generative models is a fundamental problem in statistics and machine learning. The Local Flow Matching (LFM) framework proposes a modern approach that learns a continuous and invertible flow to map noise samples to data samples. Unlike existing methods, LFM uses a simulation-free scheme and incrementally learns a sequence of Flow Matching sub-models. This iterative process reduces the gap between the two distributions and enables smaller models with faster training times. The proposed framework demonstrates improved training efficiency and competitive generative performance on tabular data and image datasets. |
Keywords
» Artificial intelligence » Density estimation » Diffusion » Machine learning
