The difference between transfer learning and fine-tuning
1. Give an example
When we encounter a new task to solve, transfer learning and fine-tuning can help us learn and complete this task faster.
Transfer learning is like we have learned some knowledge related to the target task , and then we can apply this knowledge to new tasks .
By analogy, it is as if we have learned the skills of drawing cats before, and now we want to draw a dog, we can borrow the knowledge and skills we learned before to draw the dog better.
Fine-tuning is a specific method of transfer learning. Its idea is to use the trained model to help us complete new tasks. It's like we have drawn a basic outline drawing, and then according to specific requirements, we fine-tune and correct some parts to make it more in line with the object we want to draw.
Therefore, both transfer learning and fine-tuning are to make better use of previously learned knowledge and experience on new tasks, so as to improve the learning effect and performance of new tasks.
2. Concept description
Transfer Learning and Fine-tuning are two common machine learning methods for using a trained model on one task to improve the performance of another related task. They have some differences, here are their explanations and differences:
2.1 Transfer Learning
Transfer learning refers to utilizing knowledge and models learned from a related source task when solving a target task . Typically, source tasks are trained on large-scale data and have high performance. The goal of transfer learning is to apply the knowledge of the source task to the target task, thereby speeding up the training process of the target task or improving the performance of the target task.
Transfer learning usually consists of two steps:
1. Pre-training phase: Train a model on the source task, usually on large-scale data, such as using a large neural network to train on the ImageNet dataset.
2. Fine-tuning stage: use the pre-trained model as the initial model, and perform further training on the dataset of the target task. In the fine-tuning stage, the parameters of the model can be adjusted according to the data and specific requirements of the target task to adapt it to the target task.
The advantage of transfer learning is that it can use existing models and data, thereby reducing the training time and sample requirements of the target task. It is especially suitable for situations where the target task data is small.
2.2 Fine-tuning
Fine-tuning is a specific method in transfer learning , which is used to adapt the model to the target task on the basis of the source task. During fine-tuning, it is common to unfreeze some or all layer parameters of a pre-trained model and further train these layers using the dataset of the target task.
The fine-tuning steps include:
1. Freezing phase: Lock the parameters of the pre-trained model to prevent it from being updated.
2. Unfreezing phase: Unlock some or all layer parameters of the pre-trained model so that it can be fine-tuned on the data of the target task.
The goal of fine-tuning is to adapt the pre-trained model to the characteristics and data distribution of the target task through limited training on the target task. Through fine-tuning, the model can be quickly adjusted with a small amount of target task data to achieve better performance.