  We present a proof of concept for mining JWST imaging data for anomalous
galaxy populations using a conditional Generative Adversarial Network (cGAN).
We train our model to predict long wavelength NIRcam fluxes (LW: F277W, F356W,
F444W between 2.4 to 5.0\mu m) from short wavelength fluxes (SW: F115W, F150W,
F200W between 0.6 to 2.3\mu m) in approximately 2000 galaxies. We test the cGAN
on a population of 37 Extremely Red Objects (EROs) discovered by the CEERS JWST
Team arXiv:2305.14418. Despite their red long wavelength colours, the EROs have
blue short wavelength colours (F150W \- F200W equivalently 0 mag) indicative of
bimodal SEDs. Surprisingly, given their unusual SEDs, we find that the cGAN
accurately predicts the LW NIRcam fluxes of the EROs. However, it fails to
predict LW fluxes for other rare astronomical objects, such as a merger between
two galaxies, suggesting that the cGAN can be used to detect some anomalies
