5 Amazing Examples of Transfer Learning in Use

Machine learning is omnipresent in almost every industry today due to its predictive solutions that include intelligence development and reliable models. However, training these models in the right way is a strenuous task, as it requires creating labeled data within the model before getting it ready. Fortunately, the time and hard work can be reduced with the use of transfer learning. It is a smart and effective type of machine learning that can use different learning scenarios to apply to related problems.

Now you would ask what is transfer learning. Well, this article is everything that you need to know about transfer learning. So, let’s get started!

What Is Transfer Learning?

Transfer learning is a trained machine learning method, which is applied to a different yet related problem. For instance, if you have trained a simple classifier to detect whether images contain a bag, you can use a similar model to predict other objects like a wallet. 

Transfer learning is simply using one model trained to do one task and exploit it to do others and improve generalization. In short, it is a machine learning technique where a method developed for a task/problem is reused on another task. It begins with patterns that have been learned from performing a task.

Typically, transfer learning is used in natural language processing and computer vision-related tasks such as sentiment analysis. It is important to understand that transfer learning is not machine learning but a design methodology like active learning. It is not a study area or exclusive part, but related to problems like concept drift and multi-task learning. In machine learning, concept drift means that the statistical properties of a task/problem, which the model is trying to predict, change in unforeseen ways over time. As a result, it leads to predictions becoming less accurate. Transfer learning comes in handy at this stage as it uses a tremendous amount of data and information for making the right predictions. So, in combination with neural networks, transfer learning has become highly popular as it requires a humongous amount of data.

How Does Transfer Learning Works?

Neural networks are used to detect the edges in layers, shapes in the middle layer, and certain base layer features. Only the early and middle layers are used in transfer learning. It helps in leveraging labeled data for the task it was trained for.

There are different tools used in training these models. For instance, Keras transfer learning is preferred for better flexibility and scalability. Also, if the original model was trained using transfer learning TensorFlow, it can be restored and retrained for other layers of the task. Pytorch transfer learning is more of deep learning and has a practical approach to everything.

Let’s take transfer of learning examples of a simple classifier trained for detecting a bag on an image. But this time, the model will be used to identify sunglasses. The early layers of this model are trained to identify objects, so it is better to retrain the latter layers to train the models so that it can identify what separates sunglasses from others.

With transfer learning, maximum knowledge is transferred from the model onto a new task. The knowledge is transferred as much as possible from the previous task to the new task at hand. Depending on the problem and the data, this knowledge can be in numerous forms. 

Transfer Learning Examples in Use

Transfer learning is being used in different verticals and making groundbreaking advancements. Here are a few transfer learning examples that you must be aware of:

#1 Real-World Simulations

If you are looking for real-world implementations, you should go for digital simulation to create a physical prototype. Real-world training of the robots can be time-consuming and expensive. Hence, it is better to train them using simulations. Progressive networks are used for simulations in robot control domains. Simulations are also used in self-driving cars as well which, in it’s turn, are trained through video games.

#2 Gaming

With artificial intelligence, the gaming world has been taken to the next level. The gaming industry has successfully implemented transfer learning to create highly effective gaming models. DeepMind’s neural network program, namely AlphaGo, is the perfect example of it. Due to transfer learning, the methods learned in the game can be applied to another game. MadRTS is another great example of transfer learning in gaming, which is a real-time strategy game that is used to carry out simulations.

#3 Image Classification

As discussed in our first example, image classification is the most common way to use transfer learning. With the help of neural networks, these models can recognize different objects in an image. They are trained to go through huge data sets and make the task easier. It reduces the time for training by pre-training it with ImageNet that has many images in different categories. Medical imaging is the ideal example of image classification where a model is trained via ImageNet to detect kidney issues in an ultrasound. Image classification was the first to be used in transfer learning.

#4 Zero-Shot Translation

It is an extended version of supervised training where the model’s goal is to predict values that were not present in the training dataset. The working example of zero-shot translation is the Neural Translation model (GNMT) by Google that offers cross-lingual translations. There will be two discreet languages that need to be translated with a pivot language. For instance, if you want to translate Korean to Japanese, you first need to transfer Korean to English and then English to Japan. This translation uses data to learn the translation techniques for translating the language pair.

#5 Sentiment Classification

It is important to understand customer behavior in order to generate more sales. Thanks to transfer learning, businesses can now understand their customers better with the help of sentiment analysis that studies subjective data in expressions. This includes what they like, their dislikes, their interests, the device they use, their views on a particular product or service, etc. It helps in understanding the users’ emotions behind certain feedback or review. With the automated process of sentiment classification, opinions from customers can be converted into texts that will decide whether they are positive, negative or neutral. After making this analysis, businesses can make customized plans for their customers and enhance their experience. The best example of sentiment classification is social media monitoring wherein businesses can mine data and extract opinions from the social media conversations. 

What Are The Types of Transfer Learning?

Different types of transfer learning are used in different scenarios. Varied forms of transfer learning are used to deal with similar problems but are extremely different.

Let’s learn about the five different types of transfer learning:

1. Domain Adaption

It refers to the situation when the marginal probabilities among the target and source domain are varied. There will be a need for shift and drift in the data distribution to transfer the learning. For instance, a movie review labeled positive or negative is entirely different from a product review. So a model used for classifying movie reviews will work differently in product review sentiment. In such situations, domain adaptation methods are used in transfer learning.

2. Domain Confusion

There are different sets of features in varied layers of the deep learning network. This can be used to understand the domain-invariant features and enhance transferability. Specific processing steps are applied to the model in order to make both domains similar. Instead of making the model learn any representation, it is essential to nudge it to make it similar to the domains. Typically, this type of transfer learning is about adding another objective to the source and increasing the similarity.

3. Multitask Learning

It is a different type of transfer learning. In this, different tasks are learned without differentiating source and target. The learner will get all the information regarding multiple tasks all at once, which is different from traditional learning. So, instead of working on separate tasks differently, the learner has to go through multiple tasks at once. In traditional transfer learning, the learner didn’t have any information regarding the tasks. But now, it is extremely different with multitasking transfer learning.

4. One-Shot Learning

It is true that deep learning systems are always looking for data, and they need many data sets for better understanding. For instance, when you show a child an apple for the first time, they can easily detect it the next time they see an apple. In one shot learning, the output is inferred on one or a few training sessions. It comes in handy in real-world scenarios where labeling of data is not possible. One-shot learning is also an effective type of transfer learning that can yield results.

5. Zero-Shot Learning

It is yet another variant of transfer learning that does not rely on labeled examples. This may sound surprising to you in learning with examples. Zero data or zero-shot learning are meant to make smart adjustments in the training stage that help exploit additional information. This particular method is used to learn conditional probability. Hence, it is ideal in the situations of machine translations.    

The Bottom Line

Transfer learning has brought many innovations in machine learning that has further harnessed its capabilities. With the use of algorithms and applied logic, transfer learning can speed up the process. This process directly helps in reducing capital investment and time consumption. That’s the reason many organizations are thinking about applying transfer learning in their business. Transfer learning is versatile. That’s why it is being used in many verticals. The future of transfer learning seems to be bright, and it would be exciting to see how other sectors make the most of this machine learning capability.

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