Transfer learning is a machine learning technique that uses a model that has already been trained to carry out one task to carry out another that is similar to it. Zhuang et al. (2020) describe transfer learning as using pre-trained models to improve performance on a new task with a smaller dataset rather than typically needing to train a model from scratch. The objective is to quickly learn from one problem by applying what was discovered to another related problem. Wolf (2019) states that the computer vision tasks of image classification, object detection, and image segmentation are a few examples that heavily rely on transfer learning. An example of transfer learning is a model trained to recognize different animal species and can be modified to recognize particular dog breeds with much less training data. Another example is applying a model trained to identify faces to perform facial expression recognition or gender classification.

Zhuang et al. (2020) point out that natural language processing (NLP) tasks like sentiment analysis, question answering, and machine translation can also benefit from transfer learning. Also, a pre-trained language model, such as BERT or GPT-3, can be fine-tuned on a smaller dataset to perform sentiment analysis or question answering. Transfer learning can also save significant time and resources because it improves the generalization of models, requires less computing power and labeled data, and improves performance on new tasks with little data (Wolf, 2019).