I have a dataset that has 32x32x3 images. However, I want to use models that were developed for 224x224x3 images, e.g. resnet. A common theme I see is that people resize 32x32x3 to 224x224x3, but this means a CNN is scanning large segments of identical and redundantly colored space. Conceptually, this is not ideal.
One idea is to put a few layers at the beginning of the network, allowing the network to learn a mapping from 32x32x3 to 224x224x3. I have read a little bit about deconvolutions and think that might be the answer.
What is a reasonable set of deconvolution layers to go from 32x32x3 to 224x224x3?