Comments (1)
Thank you for your attention to our work!
The baseline results in Figure4 of the paper are individual points of models with various sizes (associated with input resolutions for EfficientNets, MobileNets and MnasNets). We do so since it is shown in MSDNet [1] that ensembling multiple models with early-exit results in inferior performance compared with the original ones.
[1] Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, and Kilian Q Weinberger. Multi-scale dense networks for resource efficient image classification. In ICLR, 2018.
from gfnet-pytorch.
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