提出简单高效流程匹配方法，大幅提升细胞显微图像生成质量。
Elucidating the Design Space of Flow Matching for Cellular Microscopy
- 采用简化设计的流程匹配框架，避免冗余复杂结构。
- 模型规模扩大100倍，FID降低50%，KID降低90%。
- 适配分子嵌入可模拟未知分子反应，适合生物医学生成任务。
流程匹配生成模型在模拟细胞对生物扰动响应方面日益重要，但其构建设计空间庞大且研究不足。本文系统分析了用于细胞显微图像的流程匹配模型设计空间，发现许多流行技术不仅不必要，甚至会损害性能。我们提出一种简单、稳定且可扩展的训练方案，并据此训练基础模型，其规模较先前方法大两个数量级，在相同条件下实现两倍的FID提升和十倍的KID改进。随后，通过使用预训练分子嵌入进行微调，模型在模拟未见分子响应上达到当前最优表现。代码已开源。
原文摘要 · Abstract (English)
Flow-matching generative models are increasingly used to simulate cell responses to biological perturbations. However, the design space for building such models is large and underexplored. We systematically analyse the design space of flow matching models for cell-microscopy images, finding that many popular techniques are unnecessary and can even hurt performance. We develop a simple, stable, and scalable recipe which we use to train our foundation model. We scale our model to two orders of magnitude larger than prior methods, achieving a two-fold FID and ten-fold KID improvement over prior methods. We then fine-tune our model with pre-trained molecular embeddings to achieve state-of-the-art performance simulating responses to unseen molecules. Code is available at https://github.com/valence-labs/microscopy-flow-matching
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。