Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: The authors present a general overview of few-shot learning (FSL), from different axes. They also present work on data calibration and its effect on the performance in FSL. Finally the authors offer some perspective on bias correction in the context of FSL.

Overall the post is well written. I appreciated the critical presentation of (Yang et al., 2021), with its advantages and limitations and perspectives from more recent work.

However, the post lacks some structure: although it feels like the common direction is "bias correction", the different sections seem to be unconnected. For example, the final section on bias correction is not presented from the perspective of (Yang et al., 2021), and seem like yet another overview (like the general presentation of FSL). Moreover, all of these different sections are treating from a surface level only. It would have been better to focus on only one aspect (e.g. bias correction), and to investigate that one aspect deeper. The conclusion seems to be unrelated to the main text (nothing in the post prior to that section would suggest that perspective on larger models, no matter if this is a very valid point).

The post also contains a lot of typos and grammatical errors, that need to be corrected during a revision.

I can only recommend weak acceptance. The topic is interesting, the presentation of the ICLR paper very good, but the whole structure of the post is a bit off. I would suggest the authors to downscale their general presentation of FSL in a revision, and to focus more on the connection between (Yang et al., 2021) and the topic of bias correction in FSL.

(Yand et al., 2021) Free Lunch for Few-shot Learning: Distribution Calibration. (ICLR 2021)
review_title: Review of Prior Knowledge for FSL

Meta-review: The overview of FSL and the ICLR paper is good, although the grammar can sometimes be distracting and difficult to follow. The main criticism for this post is that while there is some value added beyond sumarizing the original paper and approaches in FSL, the discussion on the limitations of Yang et al., 2021 was fairly limited, and the follow up discussion of bias correction was not explicitly linked to the previous discussion of Yang et al., 2021.

Furthermore, taking raw content as screenshots from the original paper is not recommended, especially without providing a clear reference. 
Decision: Reject