I work on the topics of sparse coding (SC) and dictionary learning (DL), in which classification is not the main goal, but the discriminative power of the representation is one of the concerns. Therefore, i use classification accuracy in order to show how my method is more discriminative than other SC or DL approaches.
Also, i use typical datasets in the literature of SC which are image datasets such as Ex.YALE, Caltech101, AR-face and similars.
But so often i'm criticized (even harshly!) by the reviewers (at least one in each submission) on why i didn't compare to deep-networks or why my results are not better than the reported CNN-method in paperX.
Or even i got feedbacks like "The datasets are small sized, why not using ImageNet and similars?!"
The thing is that my method is supposed to be compared to its family of methods (SC-based baselines) and the point of my work is to improve the model/algorithm's performance in this domain and not compared to any top-classifier on any big-data on the earth! :((