https://www.tensorflow.org/hub
Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem.
Transfer learning is usually done for tasks where your dataset has too little data to train a full-scale model from scratch.
The most common incarnation of transfer learning in the context of deep learning is the following workflow:
A last, optional step, is fine-tuning, which consists of unfreezing the entire model you obtained above (or part of it), and re-training it on the new data with a very low learning rate. This can potentially achieve meaningful improvements, by incrementally adapting the pretrained features to the new data.
Example:
When does transfer learning not work?
Transfer learning will not work when the high-level features learned by the bottom layers are not sufficient to differentiate the classes in your problem. For example, a pre-trained model may be very good at identifying a door but not whether a door is closed or open. In this case, you can use the low-level features (of the pre-trained network) instead of the high-level features. In this case, you will have to retrain more layers of the model or use features from earlier layers.
When datasets are not similar, features transfer poorly. This paper investigates the similarity of datasets in more detail. That said, as shown in the paper, initializing the network with pre-trained weights results in better performance than using random weights.
https://keras.io/guides/transfer_learning/
https://d2l.ai/chapter_computer-vision/fine-tuning.html