Transfer Learning

Transfer learning is a machine learning technique where knowledge gained from training a model on one task is transferred and applied to a different but related task. Instead of starting the learning process from scratch, transfer learning leverages the pre-trained model's learned features, representations, or parameters to expedite the training and improve the performance of the target model.

Here's a high-level overview of the transfer learning process:

Pre-training: A model is first trained on a large-scale dataset and a specific task that is typically unrelated to the target task. This pre-training phase aims to learn general features or representations from the data.

Transfer: Once the pre-training is complete, the knowledge learned by the pre-trained model is transferred to a new model, referred to as the target model. The target model is usually a neural network with some layers already initialized or adapted from the pre-trained model.

Fine-tuning: The transferred model is further trained or fine-tuned on a smaller labeled dataset specific to the target task. During this phase, the parameters of the target model are updated to make them more task-specific. However, the lower-level features or representations learned during pre-training are usually kept frozen or have their learning rate reduced to prevent them from being heavily modified.