Transfer learning is a machine learning technique that involves leveraging the knowledge learned from one task to improve the performance of another related task. In other words, a model that has been trained on one task can be used as a starting point for training a new model on a related task.
Transfer learning can be useful in situations where there is not enough data available to train a model from scratch, or where the cost of training a model from scratch is prohibitively high. Instead, a pre-trained model can be fine-tuned on a smaller dataset for a related task.
There are several ways to perform transfer learning, including feature extraction, fine-tuning, and domain adaptation. In feature extraction, the pre-trained model is used to extract relevant features from the data, which are then fed into a new model for training. In fine-tuning, the pre-trained model is used as a starting point for training a new model, and the weights of the pre-trained model are adjusted during training. In domain adaptation, the pre-trained model is adapted to a new domain by adjusting its parameters to better fit the new data.
Transfer learning has been shown to be effective in a wide range of applications, including computer vision, natural language processing, and speech recognition. It has also been used to improve the performance of models in domains where data is scarce or where the cost of data collection is high.