Transfer learning is a technique in which a pre-trained neural network, often trained on a large dataset, is reused for a new but related task. Instead of training a model from scratch, transfer learning allows you to fine-tune the pre-trained model on your specific dataset. This is especially useful when you have limited data for the target task but want to leverage the knowledge captured by the pre-trained model.
In transfer learning, you typically use a pre-trained model’s weights as the starting point, then adapt the model by re-training the final layers on the new dataset. This approach is particularly effective in domains like image classification, where large models like ResNet or VGG are trained on vast image datasets like ImageNet.
Transfer learning can save time and computational resources, and it often leads to better performance when working with small datasets, as the model already has general knowledge from the original task.