First of all, thanks for the interesting work. The experimental results are still competitive to the current SOTA works.
However, the introduction makes me confused and doubt the results. For example, in paragraph 3, you mentioned 'Prior studies suggest that using meta-learning outperformes "vanilla" nearest neighbor classification [26, 30]'. In fact, references 26 and 30 were exactly the methods that used meta-learning, as they tackled the FSL problem within a meta-learning setting. Also, in paragraph 4, you mentioned that your method achieve SOTA performance without using meta-learning, which is the weirdest part because you were already using meta-learning when you used N-way K-shot setting.
First of all, thanks for the interesting work. The experimental results are still competitive to the current SOTA works.
However, the introduction makes me confused and doubt the results. For example, in paragraph 3, you mentioned 'Prior studies suggest that using meta-learning outperformes "vanilla" nearest neighbor classification [26, 30]'. In fact, references 26 and 30 were exactly the methods that used meta-learning, as they tackled the FSL problem within a meta-learning setting. Also, in paragraph 4, you mentioned that your method achieve SOTA performance without using meta-learning, which is the weirdest part because you were already using meta-learning when you used N-way K-shot setting.