Transfer Learning Vs Fine Tuning

Transfer Learning is a machine learning technique where a model developed for a specific task is reused as the starting point for a model on a second task. This approach leverages the knowledge gained while solving one problem and applies it to a different but related problem, significantly reducing the time and resources needed for model training. It is particularly useful when there's a limited amount of labeled data for the target task, allowing practitioners to benefit from pre-trained models that have already learned useful features from a large dataset.

Fine Tuning is a process in transfer learning where a pre-trained model is further trained on a new dataset to adapt it specifically to the new task. This involves unfreezing some of the layers of the pre-trained model and continuing the training process with a smaller learning rate, allowing the model to adjust its weights based on the new data while retaining the general features it learned previously. Fine tuning helps improve the model's performance on the new task by making it more specialized, balancing the knowledge from the original dataset with the unique characteristics of the new data.