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tokens: of the complexity of modeling subtle variations. For example, improved modeling of spectral energy distributions of stellar populations could be accomplished with cycle-starnet . As another example, relating N -body simulations of dark matter distributions to large-scale galaxy data is an ideal candidate. For fields outside astronomy, medical imaging provides interesting opportunities for cycle-starnet because the data are highly dependent on the particular medical device that is used for data acquisition. These instrumental effects could be corrected either by combining simulations and clinical data or by learning the translation of data from one system to another. Last but not least, climate modeling is an area where efficiency could be improved by relating low-resolution models to high-resolution data, a domain-transfer problem where the difference between the domains is resolution. 5.2. Limitations and Future Implementations While cycle-starnet has many attractive properties, the training of cycle-starnet can be delicate and may still require some fine-tuning. The difficulty mainly arises from the adversarial training setup. Specifically, adversarial training provides competition between the autoencoder networks and the critic networks, making convergence not as straightforward as other machine-learning tasks such as supervised regression. We note that the other proposed methods, including The Payne (Ting et al. 2019 ), The Cannon (Ness et al. 2015 ), AstroNN (Leung Bovy 2019 ), and StarNet (S. Bialek et al. 2020, in preparation), are examples of supervised regression, and therefore are technically much easier to train. To train cycle-starnet , we have extensively explored numerous architectural choices and training details before deciding upon the final model. For instance, removing the adversarial losses leads to a smoother convergence, but without it, the latent representations are no longer shared, causing cross-domain translations to fail. Nevertheless, adversarial training is an active field of research in machine learning, with many new ideas proposed (Deshpande et al. 2018 ; Miyato et al. 2018 ; Donahue Simonyan 2019 ) since the original UNIT paper was published. Consequently, we expect that some of these advancements would benefit and stabilize the training of future versions of cycle-starnet . Besides the difficulty in training in an adversarial setting, we have made a few simplifying assumptions regarding the training spectra data sets. First, we assumed that the uncertainties provided by APOGEE are uncorrelated and accurately determined. How a mischaracterization of the noise may skew the training is yet to be studied. Additionally, the labels from both domains in this study span the same ranges. Early experiments suggest that having the two domains span a similar label space is an essential aspect of cycle-starnet . For a new survey, we might not know beforehand the range of the stellar labels. Knowing how to resolve this