What is Transfer Learning?
Transfer Learning is an ML technique in which we reuse a previously trained model as the basis for a new model on a different problem.
Simply said, a model learned on one job is reused on a second, similar work as an optimization that allows for faster modeling progress on the second activity.
Transfer learning can yield much greater performance than training with a little quantity of data when applied to a new task.
Transfer learning is so frequent that training a model for image or natural language processing tasks from scratch is quite unusual. Data scientists prefer to begin with a pre-trained model that already understands how to identify objects and has learned general characteristics such as edges and shapes in photos.
- Models built on the foundation of Transfer learning include AlexNet and ImageNet.
To circumvent the constraints of standard machine learning models, deep learning specialists invented transfer learning.
To reach high performance, most ML models need to be trained from the start, which can be computed costly and necessitates a huge quantity of data.
- Transfer learning is computationally efficient and aids in achieving better results with a small data set.
Additionally, traditional machine learning uses an isolated training technique, in which each model is trained separately for a given goal without relying on prior information. Transfer learning, on the other hand, leverages information from the pre-trained model to complete the job.
Lastly, Traditional ML models take longer to attain optimal performance than transfer learning models. Because models that employ previously trained model’s knowledge already know what the characteristics are. It’s faster than building neural networks from the ground up.
Transfer Learning approaches
Depending on the area of the application, the job at hand, and the availability of data, different transfer learning algorithms and approaches are used.
It is critical to have an answer to the following questions before deciding on a transfer learning strategy:
- Which aspects of knowledge may be transmitted from the source to the target?
- When should one transfer and when should one not transfer?
- How can we apply what we’ve learned from the source model to our present task?
Depending on the task area and the amount of labeled/unlabeled data provided, transfer learning procedures are often divided into three groups:
- The Transductive Transfer Learning approach is used in scenarios where the domains of the source and target activities are not identical but are related. Similarities between the source and target tasks can be found. In these cases, the source domain generally has a lot of labeled data, whereas the target domain contains solely unlabeled data.
- The source and destination domains must be the same for inductive transfer learning to operate, even if the model’s specific tasks are different.
The algorithms strive to apply the information from the source model to the target job in order to enhance it. The pre-trained model already knows the domain’s characteristics and is off to a better start than if we had to train it from scratch.
Depending on whether the source domain has labeled data or not, inductive transfer learning is separated into two subcategories. Multi-task learning and self-taught learning are examples of this.
- Inductive Transfer Learning and Unsupervised Transfer Learning are comparable. The only distinction is that the algorithms are mostly used for unsupervised jobs and both the tasks and source use unlabeled datasets.
Transfer learning models concentrate on storing and applying information learned while addressing one problem to a different but related problem.
Rather than having to train a neural network from scratch, numerous pre-trained models may be used as a starting point. These pre-trained models result in a more dependable architecture while also saving time and resources.
When there isn’t enough data for training or we need better outcomes in a short amount of time, transfer learning is utilized.