In the present day, there’s unconditionally nothing that artificial intelligence cannot accomplish. You see chatbots, self-driving cars, IoT of devices, there’s the use of AI in healthcare, banking, logistics; all of these challenges were thought impracticable until now.
Did you ever think of unlocking your phone by bringing your face near to the device? Or to transliterate audio in real-time? It is not hidden how AI has taken over mundane jobs – “AI can perform tasks that ordinarily require human intelligence.”
But with the progressions in tech, a division of machine learning became popular and that model is called “Transfer Learning”.
Consider this guide a short introduction to the concept, like we said it’s presently admired in deep and machine learning for the reason that it can train deep neural networks. You’ll take a look at:
- What the concept is about?
- How does it function?
- Why and when you should use it?
Moreover, you’ll learn the diverse approaches to transfer learning in this very guide.
So, let’s get started.
What is Transfer Learning?
Transfer learning is a technique that is applied to resolve difficult machine learning challenges. It says you can store any information gotten while solving a problem and apply the same knowledge to a different but related problem to comprehend its behavior. For instance, any knowledge gained while studying a helicopter could apply to understand an airplane.
This exercise is most commonly used in machine learning and for several reasons. Here is a scenario, you create an architecture of a model from the beginning, train it, and then rework it as per necessity; this process requires a lot of time and effort included.
The most proficient way to train a machine learning model is to make use of an architecture that has already been well-defined and calculated. This is transfer learning in a nutshell.
Let’s delve deeper into the different ways how transfer learning functions, why, and when should you use it? as well as transfer learning examples to know.
What Are the Benefits of Using Transfer Learning?
Transfer learning is a brilliant model that was developed and trained for a single task and but can be used for a secondary task.
It differs from traditional machine learning in many ways, and that you will also cover in this talk. Let’s talk about some of the chief advantages of transfer learning.
- To create and train a model from the beginning is much work, also it involved too much data. But with this method is a great technique to achieve the same results using far fewer data.
- The models that are trained using transfer learning can generalize from one task to another for the reason that they are trained to learn to recognize features that can be applied to new contexts.
- Transfer learning makes it easier to use deep learning because it is much more possible to obtain the results desired without being an expert in the field.
What Are the Different Types of Transfer Learning?
1) Domain Adaptation
Anyone of you who has worked in machine learning has bumped into some tasks of domain adaptation. Let’s say, a model build for one domain is deployed on another domain or domains to achieve something new.
For example, in computational biology, a gene predictor who is trained on identifying gene of a specific living organism, but often desires to classify the genes of another organism or even group of organisms.
In this way, the domain adaptation model uses labeled data in one or more source domains to solve new tasks in a target domain.
2) Multitask Learning
Well, that’s a good suggestion but this particular model is becoming so popular in machine learning.
The method is executed to resolve two or more tasks at once so that any similarities or differences between them can be well-taken care of.
It is based exactly on the idea of transfer learning, a model getting trained on a correlated task can gain skills to increase its ability to solve a new task.
“Multitasking must be a part of your everyday life.”
3) Zero-shot Learning
‘Zero-shot is no shot.’
This transfer learning concept is applied to crack a task to which the machine was never exposed during training. For example, you are training a model to identify birds in pictures. To spot them, the machine is trained to identify the birds on two parameters: the color brown and the hair.
Then the model is taught on multiple birds.
To further explain the concept, you may not have pictures of dogs to train the model, but the model already knows that the dogs do have the color brown and have hair on their bodies.
4) One-Shot Learning
This model is well-used in airports to check passports of thousands of people, at the border gates, or for a search of some sort.
How do you otherwise tell if the person is the same person whose passport you’re holding in your hands?
You can develop a computer vision system that can well-look at two images it has never seen before and say whether they represent the same object or not.
Key Takeaways
What to transfer?
In this initial step, you figure out what part of the information must be conveyed to the source to expand its performance.
When to transfer?
You should wish to deploy transfer learning to improve the performance results, and not degrade them. You need to know when to transfer and when not to.
How to transfer?
Once the above answers are answered, you can take a step towards finding ways to actually transferring the knowledge across tasks.
FYI
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