Atmospheric retrievals on exoplanets usually involve computationally intensive Bayesian sampling methods.
Large parameter spaces and increasingly complex atmospheric models create a computational bottleneck forcing a
trade-off between statistical sampling accuracy and model complexity. It is especially true for upcoming JWST and
ARIEL observations. We introduce ExoGAN, the Exoplanet Generative Adversarial Network, a new deep-learning
algorithm able to recognize molecular features, atmospheric trace-gas abundances, and planetary parameters using
unsupervised learning. Once trained, ExoGAN is widely applicable to a large number of instruments and planetary
types. The ExoGAN retrievals constitute a significant speed improvement over traditional retrievals and can be
used either as a final atmospheric analysis or provide prior constraints to subsequent retrieval
