transferlearning

Transfer learning is a machine learning technique where knowledge gained while solving one problem is applied to a different but related problem. It leverages information from a source domain and task to improve learning in a target domain and task, typically when the target data are limited or costly to obtain.

There are several variants. Inductive transfer learning uses labeled data in the target task; transductive transfer learning involves the same task but different domains; unsupervised transfer learning transfers knowledge between unsupervised tasks. Domain adaptation is a common form, aiming to minimize differences between source and target domains.

In practice, transfer learning often involves pretraining a model on a large source dataset and transferring the learned representations to the target task. In deep learning, this takes the form of feature extraction or fine-tuning of pretrained networks. Common examples include convolutional networks pretrained on ImageNet for new vision tasks, or transformer models pretrained on large corpora for NLP tasks.

Challenges include domain shift, negative transfer when source knowledge harms target performance, and differences in label spaces. Techniques to mitigate include freezing layers, selective fine-tuning, regularization, data augmentation, and domain adaptation strategies. Applications span computer vision, natural language processing, speech recognition, and biomedical data.