用李群构造解耦潜在空间，实现可解释的条件生成
Decafs: Disentangled Conditional adversarial Flows
- 基于李群设计新型条件生成器，解耦潜在变量
- 在MNIST和dSprites上优于StyleGAN，分子生成任务表现优秀
- 无需增加流模型维度，保持可逆性同时提升可控性
基于流的生成模型在多个领域达到顶尖性能，但其复杂的潜在嵌入导致难以解释，尤其是生成因素在潜在空间中纠缠，阻碍了可控生成。本文提出一种基于李群的新颖条件生成器，解耦出一个与流潜在空间对齐的替代潜在空间，通过对抗损失实现紧密对齐。该方法在不增加流空间维度（因可逆性要求）的前提下，实现了可解释的条件生成。模型在图像生成（包括在MNIST、dSprites上超越StyleGAN）和分子生成（使用标准数据集QM9、ZINC、MOSES）任务中均表现出色。
原文摘要 · Abstract (English)
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
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