What does wanting to use adversarial training say about the sorts of labeled and unlabeled data we have available to us? Are there cases where we could either do adversarial training, or alternately train on a different (potentially much more expensive) dataset?
What does it say about our loss function on different sorts of errors? Are there cases where we could either do adversarial training, or just use a training algorithm that uses a different loss function?
What does wanting to use adversarial training say about the sorts of labeled and unlabeled data we have available to us? Are there cases where we could either do adversarial training, or alternately train on a different (potentially much more expensive) dataset?
What does it say about our loss function on different sorts of errors? Are there cases where we could either do adversarial training, or just use a training algorithm that uses a different loss function?