We demonstrate through ablations that Mind-Eye's performance improvements over previous methods result from specialized

Thesemethodsgenerally produce networks that are not equivalent, and thus require retraining the neural network for better accuracy.

Weintroduce asimple butgeneral online learning frameworkinwhich alearner plays against an adversary in a vector-valued game that changes every round. Even though the learner'sobjectiveis not convex-concave(and so the minimax theorem does not apply), we giveasimple algorithm that can compete with the setting in which the adversary must announce their action first, with optimally diminishing regret.

This design preserves the structural properties of the source domain, thereby ensuring that the end-model upholds them.