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We present a proof of concept for mining JWST imaging data for anomalousgalaxy 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 cGANon a population of 37 Extremely Red Objects (EROs) discovered by the CEERS JWSTTeam arXiv:2305.14418. Despite their red long wavelength colours, the EROs haveblue short wavelength colours (F150W \- F200W equivalently 0 mag) indicative ofbimodal SEDs. Surprisingly, given their unusual SEDs, we find that the cGANaccurately predicts the LW NIRcam fluxes of the EROs. However, it fails topredict LW fluxes for other rare astronomical objects, such as a merger betweentwo galaxies, suggesting that the cGAN can be used to detect some anomalies

Using cGANs for Anomaly Detection: Identifying Astronomical Anomalies in JWST NIRcam Imaging