Transfer learning is a technique where a pre-trained model is used as a starting point for solving a new but related task. The idea is to leverage the knowledge learned by the pre-trained model from a large dataset and apply it to a different dataset, thereby reducing the need for extensive training on the new dataset. This approach can save both time and computational resources. One popular use case is using a pre-trained image classification model for tasks such as object detection or semantic segmentation.

Transfer learning involves reusing the learned features of an already trained model to solve a different problem in a related domain. By starting with a model trained on a large dataset, we can benefit from the general knowledge captured by the model's parameters. Fine-tuning is then performed to adapt the pre-trained model to our specific task. This method is especially useful when we have limited labeled data for our target task.

Transfer learning is when you take a pre-trained model and use it on another task. You don't have to train the model from scratch, just fine-tune it for the specific task. It's a great way to achieve good results with limited labeled data.