Humans have an inherent ability to transfer knowledge between tasks. What we acquire as knowledge while learning a task, we use it in the same way to solve related tasks. The more the tasks are linked, the easier it is for us to transfer or use our knowledge in a cross-manner. This is the idea behind Transfer Learning in Machine Learning. In this article, I will explain what transfer learning is.
What is Transfer Learning?
Until now, conventional machine learning and deep learning algorithms have traditionally been designed to operate in isolation. These algorithms are trained to solve specific tasks. Models should be rebuilt from scratch once the feature space distribution changes.
Transfer learning is an idea of overcoming an isolated machine learning model and using the knowledge acquired for a task to solve related ones. Traditional machine learning is isolated and occurs only based on specific tasks, data sets, and training on separate isolated models.
Simply put, it means using the knowledge gained while solving a problem and applying it to a different but related problem. No knowledge is kept that can be transferred from one model to another. In transfer learning, you can take advantage of the knowledge (features, weights, etc.) of previously trained models to train new models and even solve problems such as having less data for the new task.
Applications of Transfer Learning
The use of pre-trained models and associated domain data promises to supercharge most general developments for machine learning. By tapping into a pre-trained model for a purpose related to its original design, your team can skip the data cleansing, setup, and training needed to bring a model up to the task. Images and text are two common areas in which transfer learning has already proven itself.
Transfer Learning has been particularly effective with image data, and it is common to take advantage of a deep learning model trained on some large image data sets, such as ImageNet. These pre-trained models can be directly included in other new models that expect some form of image input.
With textual data, words are mapped to vectors where different words with similar meanings have similar vector representation. Pre-trained models exist to learn these representations and are widely available. These can then be incorporated into deep learning language models, both at the entry or exit stage.
Transfer learning and pre-trained models are the future of machine learning applications in general development, and therefore must be made more accessible and discoverable for all. Hope you liked this article on what transfer learning is. Please feel free to ask your valuable questions in the comments section below.