I was reading this article (Faster R-CNN: Towards Real-Time Object Detection with Regional Proposal Network) and in yellow line:
What is the meaning of fine-tuned end-to-end?
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My understanding of the sentence is: They started with a network (ImageNet) that had been trained on some other data set. They then trained this network on a new task ('region proposal') by feeding it examples from a new data set and adjusting the parameters to minimize the new loss function (i.e. using end-to-end training). Some more information about the term 'end-to-end training' can be found here and here.