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Transfer learning is a powerful tool to adapt trained neural networks to newtasks. Depending on the similarity of the original task to the new task, theselection of the cut-off layer is critical. For medical applications liketissue classification, the last layers of an object classification networkmight not be optimal. We found that on real data of human corneal tissues thebest feature representation can be found in the middle layers of theInception-v3 and in the rear layers of the VGG-19 architecture.

Transfer Learning with Human Corneal Tissues: An Analysis of Optimal Cut-Off Layer