Which technique uses a pretrained model to assist with a new but related task?

Transfer learning is the correct technique that involves utilizing a pretrained model to aid in a new but related task. This process capitalizes on the knowledge that the model has already gained from a previous task, allowing it to adapt more efficiently and effectively to the new task. By transferring the learned representations, the model can achieve better performance with less data and reduced training time compared to training from scratch.

In scenarios where labeled data is scarce for the new task, transfer learning becomes especially valuable, as it leverages the diversity and richness of the pretrained model to enhance learning outcomes. Therefore, using a pretrained model can accelerate development and improve results in applications like computer vision or natural language processing, where extensive pretraining is common.

Other techniques such as supervised learning, reinforcement learning, and unsupervised learning have different focuses. Supervised learning requires labeled data for training, reinforcement learning focuses on decision-making through trial and error, and unsupervised learning deals with discovering patterns in unlabeled data. They do not specifically involve reusing a pretrained model for a new but related task, which is the defining characteristic of transfer learning.