Transfer Learning
A machine learning method that applies knowledge and skills learned from one task to different but related tasks.
Detailed Definition
Transfer learning is a machine learning technique where knowledge gained from training a model on one task (source task) is reused to improve learning and performance on another related task (target task). This approach is especially effective when the target task has insufficient data. Pre-training of large language models (such as the GPT series) is a typical example of transfer learning, where models first learn general language understanding capabilities on massive text data, and then these capabilities can be transferred to various specific NLP downstream tasks with minimal fine-tuning. Transfer learning has become a fundamental paradigm in modern AI, enabling more efficient and effective model development.
Learning MethodsMore in this Category
Few-Shot Learning
The ability of AI models to learn new tasks with only a small number of training examples.
Reinforcement Learning
A machine learning approach where agents learn optimal behaviors through trial-and-error interactions with an environment.
Zero-Shot Learning
The ability of AI models to perform tasks without having seen specific examples during training.