Machine learning (ML), or transfer learning, is a method of using a previously trained model to perform a task that is related.
This article will explain transfer learning, its key techniques, and its applications.
What is Transfer Learning?
Transfer learning is also known as machine learning (ML). It's where a trained model that was designed for one task is used to perform another task.
The knowledge gained from task 1 is transferred to the second model, which focuses on the new task.
Human psychology is the basis of the term "transfer learning".
Consider an expert guitarist, for example. He can learn how to play other instruments like a sitar and mandolin much more easily than someone who has never played any musical instrument.
A similar approach can be used by a simple classifier to determine if an image contains a purse. This allows you to use the model's knowledge to identify other objects such as wallets and sunglasses in your next tasks.
Transfer learning is the ability to take lessons from one task and apply them to another task. The weights that an ML model holds as it solves "problem X" are transferred to a new "problem Y".
This is where the idea lies. You reprocess information from task_1, which contains training data, to complete task_2, which includes less data or labels.
Transfer learning allows the learning process to begin with patterns that are captured and then address similar tasks, rather than starting from scratch.
Transfer learning is commonly used in computer Vision (CV), and Natural Language Processing (NLP) tasks. Both CV and NLP are dependent on large datasets, and high computing power and both require high levels of computational power.
Let's take a CV task in which you train a machine-learning model to classify MRI images. The same model can be retrained to recognize images from other diseases such as brain tumors or traumatic brain injuries. Transfer learning allows for faster and more precise results.
Similar to NLP tasks, you can build models that can perform sentiment analysis on Russian texts.
Transfer Learning is Important
Transfer learning speeds up the process of training a new model, and therefore improves its performance. It is used when a model needs a lot of training and resources.
Transfer learning is used in many deep learning projects such as neural networks for NLP and CV tasks such as sentiment analysis.
It is important to note that transfer learning doesn't explicitly refer only to machine learning. It tends to be a design methodology, similar to active learning.
This does not include a specific study, but a technique that addresses problems related to multi-task learning or concept drift.
Concept drift is a term used in ML to describe the changes in the statistical properties of a task over some time that the model attempts to predict.
The model's accuracy in predicting the future is affected. Transfer learning is useful because it uses a lot of data and information to make the right predictions.
Transfer learning is also a great option when there are not enough training data. It uses weights from the first model as initial weights for the second model.
Transfer learning is based on feature generalization when features are transferred from one task to another. Datasets are therefore crucial. Transfer learning can produce optimized results if the second training dataset is identical to the first.
Transfer Learning Methods
Different approaches can be used to transfer learning. Let's take a closer look at each approach.
1. Train 'Similar Domain' Models
This technique uses transfer learning to train models from similar domains. Let's suppose you have to complete task X, but don't have enough data.
You notice that task Y is similar to task X, and you have enough data to complete the task. You can train a model for task Y, then use that model to create a new model for task X.
2. Extract Features
Another key method to transfer learning is feature extraction. Deep neural networks are trained to be automatic feature extractors.
The representations can then be exported to more advanced models after they have been run over the pre-trained models. Data science professionals use this method extensively.
3. Pre-Trained Models are Recommended
Another approach is to develop pre-trained models that take into account transfer learning variables. Experienced model development companies often have access to a large library of models that can then be used for future designs.
This means that a model that has been trained can be used to train another model, even if it is addressing a different problem.
Examples of pre-trained machine intelligence models include Google's Inception and Microsoft's MicrosoftML R packages. The ImageNet dataset is used to train the inception model. It can classify images into 1000 and more classes. The market also offers AlexNet, Oxford’s VGG Model, and Microsoft’s ResNet.
Additionally, some pre-trained models can address NLP-related data issues such as Google's word2vec Model or Stanford's GloVe Model.
Transfer Learning Process
No matter what method you use, certain steps will help you realize your transfer learning goals.
- Get pre-trained models. Organizations can access pre-trained models from their model libraries or open-source repositories. PyTorch Hub, for example, is an open-source repository of pre-trained models that can be used to accelerate the research process, from prototyping through product deployment. TensorFlow Hub, an open repository and reusable ML library, is similar. It contains several pre-trained models, which can be used to perform tasks like image classification and text embeddings.
- Freeze layers: A typical neural system shows three layers: the inner (early), middle, and later layers. Transfer learning preserves the inner and middle layers. Only the latter are retrained to use the labeled data from the task it was previously trained on. The classifier that was trained to recognize the handbag can also be used to identify wallets and sunglasses, as we saw in the previous example. We only need to train the later layers to recognize wallets and other objects since the earlier layers are already able to recognize them.
It is important to freeze layers to avoid re-initialization. Re-initialization can lead to the model losing all of its previous learnings.
Train new layers: After freezing the layers required, you must add new layers to the model to make new predictions based on the most recent dataset.
Fine-tune your model: While it is not necessary, fine-tuning your base model can make the model more efficient. This involves releasing some layers of the model and then retraining it at low learning rates to deal with a new dataset.
Transfer Learning Applications
Transfer learning is an emerging technology with applications in many areas of machine learning. It has already been used in real-world situations.
Let's take a look at the areas of transfer learning.
1. Natural Language Processing (NLP)
Natural language processing is a system that can understand and analyze human speech in text or audio files. NLP's primary goal is to improve human-machine interaction. NLP is used in day-to-day services like voice assistants, speech recognition software, and translations.
Transfer learning enhances ML models that perform NLP tasks. Transfer learning can be used to train models simultaneously to detect different language elements, phrases, or vocabularies.
Transfer learning allows models to adapt to different languages. Transfer learning allows models to be retrained and adaptable to other languages or tasks. Pre-trained models can be used to transfer their knowledge to models that can recognize linguistic syntaxes.
Google offers a GNMT (Neural Translation Model) that can do cross-lingual translations. To accomplish the task of translating, the model uses a pivot (or common language) between two distinct languages.
Let's suppose you want to translate Russian into Korean. You will first need to translate Russian into English, then English to Korean. The technique relies on the data to determine the translation mechanism to translate between two languages.
2. Computer Vision (CV)
Computer vision allows systems to deduce meaning from visual data, such as images and videos. ML algorithm trains on large data sets (images) and then refines itself to recognize objects in the images.
Transfer learning is a great option in such situations. It takes control of the reusable parts of a CV algorithm and runs it on another model.
Transfer learning is possible using models from large training datasets to apply them to smaller images. This includes identifying the edges of objects within the images. You can also determine the layers that identify edges in images and train them based on your needs.
3. Neural Networks
Deep learning is possible only by using neural networks. They are designed to replicate and simulate human brain functions.
Because of the complexity of the models that neural networks are required to train, it is a significant resource drain. Transfer learning can be used to reduce the demand for resources and make the whole process more efficient.
To fine-tune model development, several transferrable features can be moved from one network to another. Building neural nets is all about knowledge application across tasks.
Transfer learning is a great way to reduce the time it takes to develop models and data processing requirements.
It is important that the algorithm, pre-trained model, and data files are easily accessible and documented to ensure that model-driven organizations can reap the benefits of transfer learning.
Transfer learning has streamlined and improved ML capabilities. It has made model processing more efficient.
This has resulted in a reduction of capital investment and a substantial decrease in time spent on model-building.
Many businesses, across all industry verticals, are eager to apply transfer learning. This not only improves their performance but also saves time and money. It would be fascinating to see how businesses embrace transfer learning to remain relevant in the future.