Through the use of the technique known as transfer learning, machines can make use of the information they have learned from a previous task to improve their generalization about another task. It is popular in deep learning because it can train deep neural networks with very little data. Since the majority of real-world problems don’t have millions of labeled data points to train such complex models, this is very useful in the data science field.
Scientists and machine learning scientists have used a critical technique known as transfer learning to improve efficiency and lower costs in Deep learning and Natural Language Processing. We will discuss the idea of transfer learning, how it works technically, and provide a step-by-step guide to implementing it in Python in this blog.
What is Transfer Learning in AI?
When a model is created for one task, it can be used as the starting point for another task during transfer learning. When a machine learns to transfer, it uses the information it learned from a previous task to improve generalization to another task. For instance, for a model to be able to understand and translate German movie subtitles to English, we typically need to train it with thousands of German and English text corpora। Transfer learning in artificial intelligence reduces time and computational resources by using prior training to more efficiently solve new, related problems. Transfer learning uses German-BERT for representation learning and provides additional subtitle data.
The Functions of Transfer Learning
Neural networks often attempt to identify edges in the first layer, forms in the intermediate layer, and certain task-specific properties in the final layers in computer vision, for instance. We call this retraining of models “fine-tuning.” However, in the case of transfer learning, we must separate particular layers for retraining. Let’s examine feature extraction, fine-tuning, and multi-task learning—three ideas that are associated with transfer learning.
1. Feature extraction
Utilizing a pre-trained model, feature extraction entails removing significant features or representations from data. These characteristics are then fed into a new model that is targeted at a particular topic. Stated differently, feature extraction is the process of creating new features that preserve the important information from the original data while more effectively converting raw data into a collection of numerical features that a computer program can conveniently comprehend and utilize.
Sort an image, for instance. Practically speaking, this means that only the primary and intermediate layers of a model that have already been trained and contain the more generalizable knowledge are used when the model is applied to a new task.
2. Fine-Tuning
When the two objectives are not closely related, fine-tuning is frequently employed and extends beyond feature extraction. It consists of further training an already-trained model on a dataset unique to a given area. Using the knowledge acquired from training a model on a big dataset, the method applies it to a smaller dataset that is specialized to a certain domain.
Particularly common is fine-tuning in transfer learning, which reuses a model trained on one task for another, frequently with only a few modifications. The model is fine-tuned to perform better for particular tasks, increasing its effectiveness and adaptability in practical applications. It is assumed that the model has already picked up valuable skills from the initial task that it can apply to the current one.
3. Learning multitasking
Multi-task learning involves training a single model to do multiple tasks simultaneously. After a common set of early layers that uniformly handle the data, the model has distinct layers for every task. When a neural network is trained to execute many tasks by sharing some of its layers and parameters across tasks, this is referred to as MTL in deep learning.
MTL can be helpful in a variety of applications where several activities are connected or share characteristics, such as computer vision, natural language processing, and healthcare. Additionally helpful in situations where data is scarce, MTL can enhance the model’s generalization capabilities by utilizing the information that is shared among tasks.
Uses for Transfer Learning
A popular method for handling many data science tasks, such as those in computer vision and natural language processing, is transfer learning.
1. Applications in NLP:
Transfer learning greatly enhances NLP tasks. Even though the language used may change, a model developed for sentiment analysis of social media posts can be modified to assess customer reviews.
When creating NLP models, transfer learning is essential. It enables the use of pre-trained language models that have been applied to general language translation or understanding, and then refines them for particular NLP issues, including language translation or sentiment analysis.
2. Visualization of computers
Transfer learning has proven particularly successful in the realm of computer vision. Large volumes of data are needed by neural networks created in this sector to perform tasks like object detection and image categorization.
By retraining only the last layers of a model while maintaining the weights and biases of the starting and middle layers, transfer learning enables us to build new models. New models that retain the weights and biases of the early and intermediate levels of the network while only retraining the last layers can be made possible via transfer learning.
Why is Transfer Learning Necessary?
Many of the problems that arise when creating real-time machine learning models can be resolved with the aid of transfer learning. Among them are:
1. Lowering Operational expenses: By eliminating the requirement to train models from scratch—a process that can be costly due to the acquisition of data and the use of computational resources—transfer learning lowers operational expenses.
2. Domain Adaptation: Take into consideration a domain in a specialized field, such as evaluating financial reports and highlighting the salient features. It would take a long time for the model to understand the fundamentals if we were to train it from scratch. This would already be resolved with a pre-trained model. We can use this time to refine it using terms unique to the domain (KPIs, etc.).
3. Encourages R&D: By giving researchers a starting point, transfer learning speeds up R&D in machine learning. Instead of starting from scratch, researchers can concentrate on particular facets of an issue. For instance, LLMs can offer news summaries with a range of viewpoints.
4. Reduced Expenses & Resources: Every machine learning team aims to create a feasible and dependable model. Teams are unable to spend money on computational resources for every activity. Transfer learning reduces the amount of memory and GPU clusters required, which lowers the cost of storage and cloud computing.
Challenges and Best Practices
At its core, transfer learning is simply a design strategy for increasing productivity. When a model needs to be trained on a second task with little data, transfer learning excels. To avoid overfitting and improve overall accuracy, transfer learning makes use of the information of a pre-trained model. In transfer learning, some of the most typical difficulties include:
1. Data Scarcity: There’s always a need for some training data. The model may experience underfitting if the training data is very sparse or of low quality.
2. Overfitting: Overfitting is also a problem with transfer learning. An excessive amount of fine-tuning on a task may cause the model to learn task-specific properties that are poorly generalizable to new data.
3. Domain Mismatch: When there is a connection between the source and the target tasks, transfer learning is more viable. The generalizable knowledge that has been transferred might not be sufficient to complete the new task accurately if it is considerably different.
In Conclusion,
With the use of the potent technique of transfer learning, models can more effectively handle new but related issues by utilizing current knowledge. It greatly cuts down on computational expense and training time, making it particularly helpful when the data is few. In practical applications like natural language processing and computer vision, transfer learning increases accuracy and expedites deployment by leveraging pre-trained models through techniques including feature extraction, fine-tuning, and multi-task learning.
Notwithstanding obstacles like domain mismatch and data shortages, its advantages greatly exceed its drawbacks. Transfer learning will continue to be a vital component of artificial intelligence development as long as it is properly used and supported by ongoing research.
