Transfer learning is a machine learning approach in which knowledge learned from one task, dataset, or domain is reused to improve performance on another. It can reduce training data requirements and computation by starting from an already trained model or representation.
A machine learning technique in which knowledge learned from a task is reused in order to boost performance on a related task. For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
Transferring information from one machine learning task to another. For example, in multi-task learning, a single model solves multiple tasks, such as a deep model that has different output nodes for different tasks. Transfer learning might involve transferring knowledge from the solution of a simpler task to a more complex one, or involve transferring knowledge from a task where there is more data to one where there is less data. Most machine learning systems solve a single task. Transfer learning is a baby step towards artificial intelligence in which a single program can solve multiple tasks.
A machine learning technique in which a model trained on one task is adapted to perform a second related task.