Example Concept: Transfer learning in computer vision involves taking a model pre-trained on a large dataset (like ImageNet) and fine-tuning it for a specific task. This process utilizes the learned features from the pre-trained model, such as edge detection and texture recognition, which are often applicable to new tasks. By adjusting only the final layers of the model, transfer learning allows for efficient adaptation to the new task with less data and computational resources.