Standard methods for inferring planetary characteristics from exoplanets’ atmospheric spectra are slow. We present a fast machine learning method: a deep ensemble of convolutional neural networks (CNNs) that outputs mixtures of normal distributions for planetary characteristics. The architecture of our CNN was inspired by VGG networks. We train each CNN with Kullback–Leibler divergence as its loss function on simulated exoplanet’ atmospheric spectra and their auxiliary data from Ariel Data Challenge. We expect that the performance of our deep ensemble would be worse on real data because machine learning methods assume that (in this case) both simulated and real are independent and identically distributed. However, it is highly probable that probability distributions of simulated and real data differ. We suggest using an active domain adaptation (ADA) method to mitigate the difference and thus improve performance on real data. In ADA, the deep ensemble (trained with simulated data) would query real spectra (that would improve its performance most) in rounds. In each round, queried real spectra might be annotated by a slow standard method or human annotator, and the deep ensemble would use them to improve its performance.