What is Transfer Learning?

Transfer Learning: Knowledge Reuse in Machine Learning

Transfer Learning is a powerful technique used to speed up the training process and improve the performance of artificial intelligence and machine learning models. Transfer learning allows a model to reuse the knowledge learned in a previous task in another task. This approach offers great advantages, especially when we have limited data sets. In this article, we will explore what transfer learning is, how it works and how it can be applied to different use cases.

Transfer Learning is when a machine learning model uses the experience it has gained on a task on which it has been previously trained on another similar task. While traditional methods require a machine learning model to be trained from scratch for each task, transfer learning eliminates this requirement. This allows the model to learn faster and more effectively on a new task with less data by using the knowledge it has already learned on a specific task.

Transfer learning is especially important for training large language models (LLMs) and structures such as GPTs. After these models are trained on huge datasets, they can be adapted to specific tasks and reused.

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How Transfer Learning Works

Transfer learning usually consists of two basic stages:

1. Using a Pre-Trained Model: The first step in transfer learning is to use a pre-trained model on a large data set. This model is usually trained to learn general features. For example, when a neural network is trained on visual data, it learns to recognize edges, shapes and general structures.
2. Adapting to the New Task: In the second step, the model is customized for a new task. This is accomplished by freezing some layers of the model and retraining only the last layers. Thus, the model adapts to the data in the new task while retaining the information it learned in the previous task.

For example, if a model is trained for object recognition in computer vision tasks, it can later be adapted for animal species recognition with a new data set.

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Types of Transfer Learning

Transfer learning can be applied in different approaches. Here are the most common types of transfer learning:

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1. Fine-Tuning

Fine-tuning is one of the most widely used methods of transfer learning. In this method, the final layers of a pre-trained model are retrained for the new task. While the basic features learned in the previous task are retained, specific information from the new task is added to the model. The fine-tuning method is especially effective in models working with neural networks.

2. Feature Extraction

In this method, only the last layers of the pre-trained model are used and customized for a new task. The rest of the model is kept constant during the feature extraction phase. Feature extraction is often used in tasks that work on smaller data sets.

3. Domain Adaptation

Domain adaptation aims to ensure that when the model is transferred from one task to another, it adapts to the differences in data between the two tasks. For example, in a scenario where a model is trained on visual data, the model can then be adapted to work on a different data format (e.g. medical images).

Benefits of Transfer Learning

Transfer learning offers several advantages over traditional model training approaches. Here are some of these advantages:

1. High Performance with Less Data

Transfer learning can be extremely effective, especially when you have limited data sets. Instead of training a new task from scratch, using a pre-trained model allows you to achieve high performance with less data. This is an important advantage for many machine learning projects.

2. Faster Training Times

Transfer learning greatly speeds up the training process. Instead of training a model from scratch, it may be sufficient to retrain only a few layers using a pre-trained model. This saves time and transaction costs.

3. General Knowledge and Adaptation to Specific Tasks

Pre-trained models can learn general information from a large dataset. This general knowledge makes it easier to customize the model for new tasks. For example, if a language model is trained on a large amount of text data, it can then be easily adapted to a specific language processing task (e.g. text classification).

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Transfer Learning and Popular Applications

Transfer learning has many applications in the world of artificial intelligence and machine learning. Here are some common uses of transfer learning:

1. Natural Language Processing (NLP)

Transfer learning is frequently used in natural language processing (NLP). In particular, models such as GPT and BERT can be adapted to various language processing tasks after being trained on a large amount of text data. These tasks include language modeling, text classification and question answering.

2. Visual Recognition

Transfer learning is used in computer vision for tasks such as object recognition, image classification and face recognition. For example, once a model has been trained on a large image dataset, it can be easily adapted to tasks in a specific domain (e.g. medical imaging).

3. Gaming and Robotics

Transfer learning is also used in reinforcement learning. Once a robot has learned to succeed in a task, it can adapt faster by using the learned strategy in similar tasks.

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Difficulties of Transfer Learning

Although transfer learning is a powerful technique, it also presents some challenges:

• Negative Transfer: Sometimes, the knowledge of a previously trained model may not be appropriate for the new task and may negatively affect performance. This is called “negative transfer” and is a challenge to be aware of.
• Data Mismatch: Transfer learning requires a certain alignment between the source task and the target task. If the type or distribution of data between two tasks is very different, the performance of transfer learning can degrade.

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Conclusion

Transfer learning is an extremely useful technique that increases efficiency, shortens training times and reduces data requirements in machine learning and artificial intelligence projects. As Komtaş, we offer you the best solutions by using modern artificial intelligence approaches such as transfer learning in your projects. We are here to support you with the best practices in line with your needs.