Transfer Learning in Vision

Transfer learning is one of the most important practical techniques for building strong computer vision models with limited data. Representations learned on large datasets such as ImageNet can be adapted to new, smaller tasks. This reduces training cost, speeds up learning, and often improves overall performance. A large share of today’s production vision systems rely directly or indirectly on transfer learning.