The Antarctic margin is a critically under-observed region despite its importance to the global climate. Here, in-situ ocean observations are difficult to obtain and clustered in easier-to-access regions. In addition, autonomous salinity measurements have to be corrected for drift or bias after collection. In this work, we introduce a new method that uses neural networks to identify and correct errors in ocean observations. Salinity estimates from a neural network trained on ship-based data are evaluated against Argo and seal measurements around Antarctica. We find that Argo salinity observations lie within the bounds of the ship-based data uncertainty, validating existing quality control processes for Argo. However, salinity data from seal-mounted sensors has a salty bias of up to 0.13 g/kg below 250m, which peaks at 5 months since sensor deployment. Our results showcase a new, flexible and computationally efficient way to assess in-situ ocean data, with potential for global implementation.