Without reading the papers it’s hard to know for sure, but we can break down the terms and make a decent guess. Domain invariance is the ability for a model to

Without reading the papers it’s hard to know for sure, but we can break down the terms and make a decent guess. Domain invariance is the ability for a model to deal with multiple problem domains (e.g. wind conditions, type of drone, etc). Adversarial training means teaching the system to deal with different domains by deliberately giving it the hardest possible examples of different domains - where these difficult examples are in fact typically learned by looking at the gradients from the main algorithm and seeing what would cause it the most problems, and then giving it that problematic domain and forcing it to behave well. The math is similar to GANs where you have multiple neural nets effectively fighting against each other to achieve some desired outcome. Meta-learning is where you use ML to learn something about how to train your model. This can take many forms. Sometimes this means learning how an optimizer should work. Sometimes it’s a lot like fine-tuning where the meta-learner learns a kind of base model that can easily be adapted to new situations. I’d guess in this situation it’s the latter.