Review for NeurIPS paper: OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification

Correctness: In Table 1, baseline methods are thresholded at a 95% TPR, while the proposed method and its variants are claimed to be threshold-agnostic: From section 3.3 it appears that the threshold is manually set to be 0.5, so they are not really threshold agnostic. It feels likely to me that there might be situations where picking thresholds with different criteria for comparative methods might lead to an unfair assessment. I'd recommend picking an OOD-detection threshold (on the maximum softmax values for class 1 across all tasks, for example) also at 95% TPR for a more even comparison. The experiment in Section 4.4 feels a bit anecdotal due to the particular example studied. Appendix D studies the effect of the adversarial adaptation, and while the text says random-(ini)-OOD outperforms random-OOD, the table seems to show the opposite trend (a typo perhaps?), which would indicate the adversarial adaptation did not help.