Protonet re-implementation details about metaoptnet HOT 3 CLOSED

(a) For prototypical networks, we set meta-training shot == meta-test shot for all datasets. In other words, if you want to test your model on 1-shot scenario, you need to set meta-training shot to be 1.
(b) From what I remember, label smoothing was only applied for miniImageNet.

from metaoptnet.

Thanks for your response @kjunelee

I understand that the shots are matched, my question was more on the number of ways. The original protonet paper found that increasing ways improved their performance. So I was wondering if you do the same in your re-implementation with Resnet-12 or do you train only with 5-way. Also, have you tried experiments with increasing ways for the SVM and RR models?

from metaoptnet.

Sorry I misread your question.

From what I remember, in our implementation increasing ways did not help for Prototypical Networks. Hence, we always trained on 5-way for experiments in our paper. Note that we use different hyperparameters (e.g. number of query samples) from original ProtoNet paper and this may be the reason why we did not observe a gain by increasing "way". To be honest, we didn't try much on increasing the number of ways as it increases memory consumption. It might be still useful to try it. One caveat is that due to the way we implemented multi-class SVM, the running time will blow up if you try >10-way. Our ridge regression implementation does not have such problem.

from metaoptnet.