Transfer Learning using a Pre-trained Model

Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. The traditional machine learning approach generalizes unseen data based on patterns learned from the training data, whereas for transfer learning, it begins from previously learned patterns to solve a different task. In this post, we shall focus on the pre-trained model approach as it is commonly used in the field of deep learning. A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. One can use the pre-trained model as it is or use transfer learning to customize this model to a given task.