Usually the neural network training has at least 2 steps:first trained on a large set of some standard data (ImageNet, ...)and then the resulting weights are trained on a small set of my data (in this step we can train all layers or only one last layer)What is the same of 2-nd step, is it Fine-tuning or Transfer-learning? And what is the different between Fine-tuning and Transfer-learning?
What is the different between Fine-tuning and Transfer-learning?
machine learning;neural network;deep learning;computer vision;caffe
Generally, I would refer to this as transfer learning or network adaptation. That is, taking a network that has learned useful features from one domain and adapting that network and its developed features to another domain.That said, there appear to be many sources that closely conflate fine tuning with transfer learning. Therefore, I would say the difference in terminology is primarily opinion-based and suggest closure of this question on those grounds.