In transfer learning, the target dataset is smaller than the base network data, and therefore, it is different

In transfer learning, the first step is to choose a base network that has been trained on a large dataset. This base network has learned general features and patterns that can be useful for a variety of tasks.

Next, you need to prepare your target dataset, which is smaller than the base network data. This dataset should be specific to the task you want to solve.

Once you have your base network and target dataset ready, you can start the transfer learning process. The first step is to remove the last layer or layers of the base network, as these layers are task-specific and not relevant to your target dataset.

Then, you add a new layer or layers to the base network. These new layers will be trained on your target dataset. The idea is that the base network has already learned general features, and the new layers will learn to adapt these features to your specific task.

Finally, you train the entire network, including the base network and the new layers, on your target dataset. This fine-tuning process allows the network to learn task-specific features and improve its performance on your target dataset.

In summary, transfer learning is a technique that leverages the knowledge learned from a base network trained on a large dataset to solve a different task with a smaller target dataset.