How? An overview
How should we use transfer learning? There are two typical ways to go about this. The first and less timely way, is to use what is known as a pre-trained model, that is, a model that has previously been trained on a large scale dataset, for example, the ImageNet dataset. These pre-trained models are readily available across different deep learning frameworks and are often referred to as "model zoos". The choice of a pre-trained model is largely dependent on what the current task to be solved is, and on the size of the datasets. After the choice of model, we can use all of it or parts of it, as the initialized model for the actual task that we want to solve.
The other, less common way deep learning is to pretrain the model ourselves. This typically occurs when the available pretrained networks are not suitable to solve specific problems, and we have to design the network architecture ourselves. Obviously, this requires more time and effort to design the model and prepare the...
