Weakness:
Notation are sometimes confusing. (1) In Eq.3 the function d is overloaded. The function outputs the task embedding in the first part. It outputs RNN hidden representation in the second part. (2) In line 7-8 in Algorithm 1, e_j is a new variable that seems to be newly-introduced. Please also provide hints for the reader to link e_j with p(y|x_j), e.g., refer the reader to Eq. 1. Questions:
Is there any specific reason that RNN is selected as the task embedding network?
How long is the training process? How large is the final model? Some comparison in model size and training efficiency may be included.
Minor Suggestions:
Eq. 7. Is the first stage done on the support set or the query set or both?
Line 274: “w/ XY”, Table 2 “w/ X&Y”. Please keep consistent.
I believe the right side of Fig 1 is actually zooming in the part of [e_l^{task}] —> [Bottleneck Adapter]. Please consider highlighting this information to make the figure more accessible.
The authors provide detailed discussion on Grad2Task's limitations in Sec 7. I listed some of my concerns and suggestions in the main review.
1. Questions: Is there any specific reason that RNN is selected as the task embedding network? How long is the training process? How large is the final model? Some comparison in model size and training efficiency may be included. Minor Suggestions: Eq.