@article{Grover_Chute_Shu_Cao_Ermon_2020, title={AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/5820}, DOI={10.1609/aaai.v34i04.5820}, abstractNote={<p>Given datasets from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain. Variants of this problem have been studied in many contexts, such as cross-domain translation and domain adaptation. We propose AlignFlow, a generative modeling framework that models each domain via a normalizing flow. The use of normalizing flows allows for a) flexibility in specifying learning objectives via adversarial training, maximum likelihood estimation, or a hybrid of the two methods; and b) learning and exact inference of a shared representation in the latent space of the generative model. We derive a uniform set of conditions under which AlignFlow is marginally-consistent for the different learning objectives. Furthermore, we show that AlignFlow guarantees exact cycle consistency in mapping datapoints from a source domain to target and back to the source domain. Empirically, AlignFlow outperforms relevant baselines on image-to-image translation and unsupervised domain adaptation and can be used to simultaneously interpolate across the various domains using the learned representation.</p>}, number={04}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Grover, Aditya and Chute, Christopher and Shu, Rui and Cao, Zhangjie and Ermon, Stefano}, year={2020}, month={Apr.}, pages={4028-4035} }