Introduction
In machine learning the re-training and fine-tuning of both confuse the learner, that is they are the same or different. This is because the ultimate goal of both is to enhance the performance of the model. In this article, we will discuss both concepts and if you're also confused then it will be clear after you read this article.
Understanding Re-training
In the re-training, we train the pre-trained model from scratch using the same dataset that was used earlier. We need to re-train a model only if we find that the performance of the model is not up to the mark there is a significant change in the data distribution or a completely new dataset is available.
The Process of Re-training
The process of re-training a model involves following steps.
- Data Preprocessing: Data preprocessing is one of the very important and crucial steps in model training or model re-training. In this step, we clean and preprocess the dataset which involves handling missing values, removing duplicates, handling outliers, and normalization.
- Model Selection: In this step, we choose the appropriate model which is most relevant to our task.
- Training: Now, we train the model from scratch.
- Evaluation: In this step, we check the model performance using the validation techniques.
Understanding Fine-tuning
It is one of the forms of transfer learning in such learning a pre-trained model is reused as the initial stage of a model on a new task. In fine-tuning, we make minor adjustments to the parameters of pre-trained models to tailor them to a specific task. Fine-tuning is performed on the knowledge of a pre-trained model that it acquires ready and makes it more efficient than training from scratch.
For more about fine-tuning techniques, go through my article Fine-Tuning in Machine Learning.
Conclusion
The question of whether re-training is the same as fine-tuning can be answered with a definitive “No”. While both techniques aim to enhance model performance, they are fundamentally different in their approaches and applications. In re-training, we train the model from scratch and make it suitable for significant data changes or new datasets, while in fine-tuning, we select a pre-trained model, and then we make minor adjustments to the parameters of pre-trained models to tailor it to a specific task.