发表机构
Aalto University; Orion Pharma; ARF; University of Manchester; Yai Yai Ltd（阿尔托大学; 奥立安制药公司; 未提及具体中文译名，保留英文缩写; 曼彻斯特大学; 未提及具体中文译名，保留英文缩写）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对基于流的模型潜在嵌入难解释及生成因素纠缠问题，提出基于李群的新型条件生成器，通过对抗损失使替代潜在空间与潜在流空间对齐，实现可解释条件生成，在图像和分子生成任务中性能强大。
AI 中文摘要
基于流的模型在跨领域生成建模中取得了领先性能，但因其复杂的潜在嵌入难以解释。特别是潜在空间中生成因素的纠缠阻碍了可控生成。我们通过引入一种基于李群的新型条件生成器来规避此问题，该生成器解开一个替代潜在空间，并使用对抗损失使其与潜在流空间紧密对齐。我们的方法便于进行可解释的条件生成，同时无需扩大流空间维度（因其可逆性要求）。所提出的模型在条件图像（包括在MNIST、dSprites上优于StyleGAN）和分子（使用标准QM9、ZINC和MOSES）生成任务中展现出强大性能。
英文摘要
Flow-based models have established state-of-the-art performance in generative modeling across domains, but are hard to interpret due to their complex latent embeddings. In particular, the entanglement of generative factors in the latent space hinders controlled generation. We circumvent this issue by appealing to a novel conditional generator based on Lie groups that disentangles an alternative latent space, which is aligned closely with the latent flow space using an adversarial loss. Our approach facilitates interpretable conditional generation while obviating the need to expand the dimensionality of the flow space (owing to its invertibility requirements). The proposed model demonstrates strong performance across conditional image (including, outperforming StyleGAN on MNIST, dSprites) and molecule (using standard QM9, ZINC and MOSES) generation tasks
