In this post on Transfer Learning and its Applications, I will explain the concept and applications of Transfer Learning. To begin with, Transfer Learning is a type of Machine Learning that doesn’t build the model from scratch.
Understanding the Use of Transfer Learning and its Applications in various Domains
Basically, Transfer Learning is a technique that takes a pre-trained machine learning model and applies this model to an entirely new problem. Both the data and problem domain on which the model has been trained is different from the new problem. However, both of the application domains may be related in certain aspects. For instance, suppose you have a dataset of images of dogs. So you can build a machine learning model to identify dogs. Further, the same model can be used to identify horses or any other animal.
Since it is not always possible to acquire labeled data of sufficiently large volume, the traditional form of machine learning can’t be applied. Hence, Transfer Learning (TL) provides us the solution for many real-world problems. Actually, Transfer Learning is used to transfer knowledge across domains.
Types of Transfer Learning Techniques
Basically, there are two broad classifications of Transfer Learning techniques – homogeneous, and heterogeneous. While homogeneous transfer learning refers to the learning involving problem domains of the same feature space. Therefore, domains differ only in marginal distributions.
In contrast, heterogeneous transfer learning is applicable when domains differ in feature space. Hence, it is more difficult than the homogeneous transfer learning.
Application Areas of Transfer Learning
The following list describes some of the applications of Transfer Learning.
- Text and Image Classification
- Training the self-driving vehicles using simulations
- Robot training
- Medical Image Analysis
- AI Games
- Sentiment Analysis
- Identifying and filtering spam emails
- Speech recognition
Benefits of Transfer Learning
In fact, training a machine learning model is a time-consuming task. Sometimes it takes even days when the model is sufficiently trained. Also, model training is resource-intensive. Therefore, Transfer Learning provides us the most prominent benefit of utilizing a previously trained model. In short, Transfer Learning makes many computer vision and AI tasks simplified.
Likewise, we can reduce both time and cost of prediction by using Transfer Learning. Moreover, predictions done using Transfer Learning are more accurate if both the problems have a certain resemblance. Lastly, a large dataset is not required since the model is already trained. Therefore, Transfer Learning also saves the data collection and preparation time.
To sum up, this article on Transfer Learning and its Applications describes the concept of Transfer Learning as well as its benefits. Also, a number of applications where we can apply it are also listed. However, Transfer Learning is not required in all situations. For instance, if we have a large labelled dataset, then we should apply any of the traditional machine learning techniques. Because we get more accurate predictions in such a case. Another scenario is where the two application domains are totally unrelated. In such a case we should use a traditional machine learning technique rather than using Transfer Learning.