  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 recognise 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.
