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Transfer Learning In AI: Is it Good Or Bad?

Today is veritably the time when technology is giving a fierce competition to humans. Name an activity that the homo sapiens can do, and the AI will take it as a challenge to beat the humans. Today is the time that is seeing the best of artificial intelligence. It is surprising to see how humans made technology and how the latter is replacing the former in many cognitive tasks. Nobody could have thought a few years ago that something called artificial intelligence will be able to detect and recognize faces in images, help doctors detect cancers in x-ray scans, and talk to depressed folks like a dear friend until they come out of their loop of loneliness.

Yet, take a more in-depth look at the facts and what all causes made all this come to life. It’s the neural network advancements and deep learning developments that have made all these miracles happen. Neural networks and deep learning could be defined as a unique branch of AI that has contributed tremendously to aiding computers to solve such issues.

Everything great comes with a cost. This branch of AI requires vast amounts of data, which not everyone can afford. Training deep learning models also demands a lot of time. But hey, has technology ever run short of solutions? Transfer learning is what comes to rescue in such a situation. To simply put it, transfer learning is the discipline of making use of the knowledge gained by a well-trained AI model for another model. This technique has relieved the stress of many developers.

What Does It Take To Train Deep Learning Models?

Machine learning is a parent branch of deep learning. Earlier, deep learning and neural networks weren’t given much importance. But, vast amounts of data made it possible for neural networks to shine, and thus, deep learning algorithms took birth.

The main point is, what does it take to train deep learning models? Well, training a deep learning model requires one to provide a vast number of annotated examples to a neural network. The examples can vary from labelled images to mammogram scans of patients. This makes the neural networks to compare and analyze all these examples. It then creates mathematical models that showcase recurring patterns of a similar category of images. Without the presence of multiple examples, it is not easy to train a deep learning model well.

Now, you may be wondering what sources of images are available to make this task possible. The world is full of options that provide a great deal of image information. One of such is ImageNet. It is a database that carries more than 14 million images that are labelled in around 22,000 clear categories. Another good source is MNIST, which is a dataset of 60,000 handwritten digits. AI engineers make use of such sources to make deep learning possible.

A vast amount of data isn’t the only ingredient in the recipe for deep learning. You also need powerful computing resources. You may need a cluster of CPUs, GPUs, or Google’s Tensor Processors (TPUs). All this requires one to have a high budget, and not every organization can afford it.

Transfer Learning Is The Easier Route

AI engineers experience enormous complexities in the deep training processes. They would first need a source that provides large databases. Databases like ImageNet give some relief to them, but there are still other things to consider. The computer resources require them to spend a lot.

Instead, developers can make use of pre-trained deep learning models; and then finetune them according to their needs. This is the core of the concept of transfer learning.

The Working Of Transfer Learning

Neural networks work in a hierarchical manner. All of its networks develop in a hierarchy. Each of these networks comprises multiple layers. Once the training is complete, every layer gets tuned to detect features in the input data.  Transfer learning enables AI experts to freeze the initial layers of the pre-trained neural network. Such layers detect some of the general features that are common across all domains. Now, the engineers are left with the task of fine tuning the deeper layers with their own examples. A fascinating name is given to these AI models. The pre-trained AI model is called the “teacher,” and the fine tuned AI model is called the “student.” Clearly, the knowledge of the “teacher” is used to train the “student.”

An essential factor to consider is the similarities between the source and destination. This determines the number of fine tuned and frozen layers. If the fine tuned AI model is able to solve a problem that is similar to that of the pre-trained model, then the finetuning of the layers of the teacher model is not required.

However, if there are not many similarities between the destination and source, the engineers may require to unfreeze some layers in the teacher model. Now, they would be required to add the new classification layer. Next, they would finetune the unfrozen layers with the help of some new examples.

In cases of vast differences between the two models, the engineers might need to unfreeze and retrain the complete neural network. This kind of transfer requires multiple training examples.

There are many who raise a brow for transfer learning. However, in practical terms, this superb technique is efficient in saving a lot of time, effort, and compute resources.

Some Loopholes To Consider

While transfer learning is a boon that saves much time, effort, and resources, there are some things to consider before heading towards it. If the teacher AI model has some security issues or holes, then the student model is likely to inherit all those holes.

If a base model isn’t as sharp against adversarial attacks, then there are possibilities for the student models to be in threat of malware attacks.

Secondly, transfer learning is limited to some areas. One such area is training AI to play games. This is because such areas require reinforcement learning. For those who don’t know what this learning means, you can say that this type requires multiple trials and errors. In reinforcement learning, almost all problems are unique and thus require unique training processes.

Yet, it isn’t wrong to consider transfer learning as a great shortcut if implemented in a wise way. Deep learning applications like natural language processing become so much easier with the help of transfer learning.

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