To my understanding, transfer learning helps to incorporate data from other related datasets and achieve the task with less labelled data (maybe in 100s of images per category).
Few-shot learning seems to do the same, with maybe 5-20 images per category. Is that the only difference?
In both cases, we initially train the neural network with a large dataset, then fine-tune it with our custom datasets.
So, how is few-shot learning different from transfer learning?