Transfer learning is _the_ way to do image classification for most kinds of images in 2018, and is covered heavily in most classes. In the fast.ai class, you use transfer learning in the very first lesson to build a dog/cat classifier. Takes less than an hour to get to 97+% accuracy with no prior knowledge of deep learning.
Are you really arguing that you think transfer learning would be useful from handwriting models to turbine failure models?
Using techniques that are successful with image classification as an example and generalizing to other domains that don’t look much like imaging seems like a stretch to me.
But perhaps I’ve missed some more convincing examples of the state of the art in transfer learning.
It's hard to imagine there's anything to learn from handwriting images that could apply to turbine failure; a much broader kind of multi-task model than anything well see for awhile.
Getting a useful dog/cat classifier out of something that has been trained to differentiate between different kinds of boats instead of different kinds of mammals would be closer to what the OP aimed at.
That last one sounds pretty similar to your second sentence. Given what we know about transfer learning and CNN's, if we had a massive boat dataset, I bet it could be re-purposed to do pretty well at cat/dog.
That's worth proving / disproving.