Decafs: Disentangled Conditional adversarial Flows

Abstract: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

Machine Learning (cs.LG)

arXiv:2607.18755 [cs.LG]

(or arXiv:2607.18755v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.18755 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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