We human beings don't learn new things from scratch. Instead, we take advantage of what we have learned as much as possible, consciously or not. Transfer learning in AI attempts to do the same thing—it's a technique that takes a normally small piece of a big trained model and reuses it in a new model for a related task, without the need to access the large training data and computing resources to train the original model. Overall, transfer learning is still an open problem in AI, since in many situations, what takes human beings only a few examples of trial-and-errors before learning to grasp something new would take AI a lot more time to train and learn. But in the field of image recognition, transfer learning has proven to be very effective.
Modern deep learning models for image recognition are typically deep neural networks...
