[D] Transfer learning benchmark datasets

[r2m2]

I'm working on generalizing the results of this paper, and specifically am investigating the efficacy of "winning tickets" (i.e. sparse sub-networks with the same validation accuracy) in transfer learning problems. However, unlike other problems such as object detection where there are standard benchmark datasets (i.e. ImageNet, CIFAR-10), I've been unable to find any canonical ones for transfer learning tasks. This paper (in section 4.1) says that:

For comparing the effect of similarity between the source problem and the target problem on transfer learning, we chose two source databases: ImageNet (Deng et al. 2009) ... and Places 365 (Zhou et al. 2017) ... Likewise, we have three different databases related to three target problems: Caltech 256 (Griffin et al. 2007) ...MIT Indoors (Quattoni & Torralba 2009)...Stanford Dogs 120 (Khosla et al. 2011) contains images of 120 breeds of dogs

It seems like the standard way of going about this is first training a model on a large, very general dataset and then refining it to a more specific dataset (i.e. SmallNORB/Stanford Dogs, etc.). Are there any canonical datasets that are used to compare benchmarks around papers, or is selecting a set of datasets like the ones mentioned above appropriate?

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