+ Intuitive and appealingly elegant method, that is simple and fast.
+ Authors provide several interpretations which draw connections drawn to other methods and help the reader understand well.
+ Some design choices are well explained , e.g. Euclidean distance outperforms cosine for good reason.
+ Good results
- Some other design decisions (normalisation; number of training classes per episode, etc) less well explained. How much of good results is the proposed method per-se, and how much of it is tuning this stuff?
- Why the zero-shot part specifically works so well should be better explained. Details:
- Recent work also has 54-56% on CUB. (Chanpinyo et al, CVPRâ16, Zhang & Salgrama ECCVâ16)
- This may not necessarily reduce the novelty of this work, but the use of mean of feature vectors from the same class has been proposed for the enhancement over training general (not few-shot specifically) classification model. [A Discriminative Feature Learning Approach for Deep Face Recognition, Wen et al, ECCV 2016]. The âcenterâ in the above paper matches âprototypeâ. Probably this connection should be cited.
- For the results of zero-shot learning on CUB dataset, i.e., Table 3 page 7, the meta-data used here are âattributeâ. This is good for fair comparison. However, from the perspective of getting better performance, better meta-data embeddings options are available. Refer to table 1 in âLearning Deep Representations of Fine-Grained Visual Descriptions, Reed et al, CVPR 2016â. It would be interesting to know the performance of the proposed method when it is equipped with better meta-data embeddings.
Update: Thanks to the authors for their response to the reviews. I think this paper is acceptable for NIPS.
- For the results of zero-shot learning on CUB dataset, i.e., Table 3 page 7, the meta-data used here are âattributeâ. This is good for fair comparison. However, from the perspective of getting better performance, better meta-data embeddings options are available. Refer to table 1 in âLearning Deep Representations of Fine-Grained Visual Descriptions, Reed et al, CVPR 2016â. It would be interesting to know the performance of the proposed method when it is equipped with better meta-data embeddings. Update: Thanks to the authors for their response to the reviews. I think this paper is acceptable for NIPS.