Reusing a previously trained model on a new problem is known as transfer learning. Presently, its ability to train deep neural networks with relatively minimal data has made it quite popular in the deep learning space.
Further, this is helpful in the data science industry since most real-world problems do not generally have millions of labeled data points to train such complex models.
However, we’ll examine the definition of transfer learning, how it works, when, and why you should use it. We’ll also go over the various transfer learning strategies.
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What is Transfer Learning?
Transfer learning is the process of applying the knowledge of a previously trained machine learning model to a new, related task.
For instance, if you trained a basic classifier to determine whether a picture contains a backpack, you could utilize the knowledge the model learned during training to identify other things, like sunglasses.
Essentially, the goal of transfer learning is to use knowledge gained from one task to enhance generalization in another. Simply put, we move the weights learned by a network at “task A” to a new task called “task B.”
Ultimately, the goal is to apply the model’s knowledge from a task with a large amount of labeled training data to a new task with less data. So, instead of commencing the learning process anew, we begin by using patterns acquired by solving a related problem.
Due to the enormous computational power required, transfer learning is typically employed in computer vision and natural language processing tasks such as sentiment analysis.
Although transfer learning isn’t strictly a machine learning technique, it is sometimes referred to as a “design methodology” in the field, such as active learning. Furthermore, it is neither an exclusive area of study nor a component of machine learning.
However, it has gained a lot of traction when combined with neural networks, which demand enormous amounts of computational power and data.
Why Use Transfer Learning?
While there are many advantages to transfer learning, the three primary ones are reduced training time, improved neural network performance (for the most part), and reduced data requirements.
To train a neural network from the beginning, a large amount of data is typically required, but access to that data isn’t always possible. This is when transfer learning becomes useful.
Because the model has previously been pre-trained, transfer learning allows you to build strong machine-learning models with relatively minimal training data. This is particularly useful for natural language processing. The reason is that producing huge labeled data sets mostly requires expert knowledge.
Furthermore, training time is shortened because building a deep neural network from scratch on a complicated task can occasionally take days or even weeks.
Read Also: PySpark Machine Learning- An Introduction
How Does Transfer Learning Work?
Neural networks, for instance, are typically used in computer vision to identify edges in the lower levels, shapes in the middle layer, and certain task-specific properties in the latter layers.
The early and middle layers are used in transfer learning, and only the later layers are retrained. So, it makes use of the task’s labeled data from its initial training.
Rewinding to the previous example of a model trained to detect sunglasses from an image by identifying a backpack. We will merely retrain the latter layers of the model to enable it to distinguish between sunglasses and other objects. This is because it has already learned to recognize objects in the early layers.
In transfer learning, we attempt to transfer as much information as we can from the task the model was trained on earlier to the current task. Depending on the issue and the available data, this knowledge might take on multiple forms.
For instance, the way models are put together might make it easier for humans to recognize new objects.
When Should You Use Transfer Learning?
Like it is in machine learning, making rules that are typically applicable is difficult. However, the following are a few guidelines about when you may use transfer learning:
- There is insufficient labeled training information to train your network afresh.
- When there’s an already existing network that is previously trained on a similar task, which is often trained on large amounts of data.
- Finally, when Task A and Task B have the same input.
If an open-source library like TensorFlow trained the original model, you can easily restore it and retrain some layers for your task. But remember that transfer learning works only if general properties are learned from the first task. Thus, they can also be helpful for other related tasks.
Additionally, the model’s input must have the same size as it was originally trained with. If you lack that, incorporate a pre-processing step to resize your input to the required size.
Transfer Learning Examples
Transfer learning is a powerful technique in machine learning where knowledge gained from one task is applied to a different but related task. Consider it as taking the skills you learned riding a bike and applying them to riding a scooter – the underlying principles are similar, making it easier to pick up the new skill.
Moreover, here are some real-world examples of transfer learning in action:
- Image Recognition
Task A: Train a deep-learning model to identify cats and dogs in images.
Task B: Use the same model, with some fine-tuning, to identify different breeds of cats or dogs.
The model has already learned valuable features about shapes, textures, and patterns from the first task, which gives it a head start in the second task.
- Natural Language Processing
Task A: Train a language model to predict the next word in a sentence.
Task B: Use the same model to build a chatbot that can answer customer questions.
The model’s understanding of grammar, syntax, and word relationships from the first task helps it generate more coherent and meaningful responses in the second task.
- Medical Diagnosis
Task A: Train a model to analyze medical images (X-rays, MRIs) for specific diseases.
Task B: Use the same model to identify early signs of other diseases or abnormalities in similar images.
The model’s ability to recognize patterns and subtle differences in the images from the first task can be crucial for early detection and diagnosis in the second task.
These are only a few examples, and the possibilities for transfer learning are vast. It’s a powerful tool that can save time and resources and improve the performance of machine-learning models across various domains.
Conclusion
We’ve explored transfer learning, a technique that aids deep learning. It’s no longer about training models from scratch in isolation. Now, it’s about leveraging existing knowledge and taking that extra leap of learning from experience.
Additionally, transfer learning is a reminder that even in the world of machines, learning is a continuous journey where knowledge accumulates and builds upon itself.