What is Transfer Learning?
Transfer learning is a machine learning technique where an already trained model is utilized to solve a different but related problem. The concept of transfer learning is to leverage the knowledge gained from a previously trained algorithm to help another algorithm solve a related problem efficiently, quickly, and accurately. Transfer learning is a valuable tool for machine learning because it allows developers, researchers, and designers to train accurate models for new problems in a more straightforward manner than traditional machine learning methods.
How does Transfer Learning Work?
In traditional machine learning, data is gathered, preprocessed, and fed through the model for training; the process needs to be repeated while adjusting model parameters until a satisfactory result is obtained. In contrast, transfer learning uses pre-trained models, which are capable of recognizing complex patterns in datasets using deep learning algorithms. These pre-trained models are trained on vast and complex datasets, labeled for specific tasks, such as object detection or speech recognition, which means they have learned features useful for other tasks as well. These models learn weights for each neuron in the neural network that also helps recognize similar features in other datasets. Transfer learning uses these already trained models' weights and internal representation for a new task instead of starting from scratch.
Why is Transfer Learning Useful?
Transfer learning is an excellent technique for machine learning tasks when working with a limited amount of data, specific hardware constraints, or for tasks that share fundamental problems with similar labeled datasets. Transfer learning saves time, computational resources and improves the model's accuracy by utilizing pre-trained models. For example, a pre-trained model that can recognize a digit from an image can be quickly re-purposed to identify the category of an object, such as a flower, car, or a bird.
Types of Transfer Learning
Inductive Transfer
Inductive transfer involves leveraging an already pre-trained algorithm on a particular task, fine-tuning it on a new dataset, and adapting it to the new task. In this method, an already pre-trained model acts as the starting point, and weights learned from previous operations are retained and built upon as it is fine-tuned for the new target task. Fine-tuning is one way to modify the pre-trained model to solve the new task efficiently.
Transductive Transfer
Transductive transfer, on the other hand, relies on the fact that the target data is similar to the source domain while excluding the domain shift. In general terms, domains refer to data distribution or characteristics of datasets. In transductive transfer, data from both the source and target domain are used to develop a unified representation. Thus, transductive transfer helps in solving novel problems by utilizing the available data.
Unsupervised Transfer
Unsupervised transfer learning involves creating a neural network that can train on data without being explicitly trained by a human from labeled data. In this transfer learning technique, the machine can learn from a vast amount of unlabeled data by sorting it based on patterns or groups. Once the machine has identified the patterns, it gets trained with labeled data, and the weights learned from the patterns are applied to the new task.
Use Cases of Transfer Learning
Transfer learning is highly useful in a variety of machine learning applications such as natural language processing, image classification, voice recognition, and text analysis. For instance, many image-based tasks such as object detection, image segmentation, and facial recognition require the model to learn various features, including color, texture, and shapes of objects in the image. When working with these models, transfer learning can help by leveraging pre-trained models that can accurately identify these features with a much-reduced computational cost.
Another use case for transfer learning is language modeling where pre-trained models are fine-tuned for a specific language by adding new data sets and input data. Examples of transfer learning models used for language models include BERT or GPT-2, which have revolutionized NLP tasks such as language translation, chatbots, and text summaries by providing an efficient way to reuse pre-existing models.
Transfer learning is a fantastic machine learning technique that enables a model trained for one task to be repurposed for another. This method utilizes the knowledge already gained by the existing model to solve a related problem efficiently and effectively with less computational effort, less time spent on pre-processing data, and less reliance on large, high-quality datasets. Transfer learning is a versatile technique with numerous use cases in diverse applications such as image classification, natural language processing, and speech recognition, among others. Machine learning practitioners can benefit tremendously from transfer learning by leveraging the already learned features and weights in pre-trained models to solve new problems, which could take significant amounts of time, data, and computational resources using conventional machine learning methods.
