Which machine learning technique involves fine-tuning a pre-trained model for a related task?

Transfer learning is a machine learning technique that involves leveraging a pre-trained model that has already learned features from a large dataset. By fine-tuning this model on a related but specific task, one can achieve better performance, especially when the available labeled data for the new task is limited. This approach is efficient and saves resources because it builds upon the knowledge acquired from the original dataset rather than starting from scratch.

For instance, a model trained on a vast dataset (such as ImageNet) to recognize a wide range of objects can be fine-tuned for a specific application, such as identifying specific types of animals or medical conditions. This is advantageous since the model already has learned general features that can be adapted to the new context.

Other techniques mentioned, like feature selection, focus on identifying and utilizing the most relevant features of a dataset rather than adapting a pre-trained model. Instruction-based fine-tuning is a more specific term related to guiding a model's training, which may not generally encompass the broader practice of transfer learning. Finally, model validation pertains to the process of assessing the performance of a model, which is not directly related to the concept of adapting a pre-existing model for a new task. Therefore, transfer learning stands out as the correct choice for this question