What is Transfer Learning?
In machine learning, transfer learning refers to the method where a model is reused as the starting point for a model on a second task.
Transfer learning makes the process of training models easier as it uses pre-trained models as the starting point for computer vision and natural language processing tasks given the vast computing and time resources required to develop neural network models.
In traditional supervised learning, the model breaks down when developers do not have sufficient trained labeled data for the task or problems thereby they rely on transfer learning models. As with transfer learning, an existing model can be used to build a new model to solve problems that are correlated. The key concept behind transfer learning in data science and machine learning is deep learning models. They require huge data, which, if your model is also managed, means that you need a lot of labeled data. Labeling data samples is a very tedious and time-consuming process in machine learning.
For example, if we want to train a model to detect animals in a park, we could apply a model that has been trained on a similar domain, e.g. wild animals entering the residential areas. But, if we want to train a model to perform a new task, such as detecting skateboarders, we cannot even reuse an existing model, as the labels between both the task/problems differ.
Transfer learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned.
~ Handbook of Research on Machine Learning Applications
How to Use Transfer Learning?
Transfer learning in machine learning and deep learning is used while building a new model to solve a new task/problem, which might take a huge amount of resources and training. In transfer learning, we take the relevant parts/classifiers from the existing machine learning model to solve a new but similar problem. This means it generalizes and finds the commonness between both the models and replicates the models for reuse. It simply denotes that knowledge or labels are transferred for different use cases and scenarios.
To avoid the rigidity and stiffness of the models, data scientists train datasets and models to be more generalized so that they can be reused for another model instead of working on it up fresh.
Transfer learning is generally utilized:
1. To save time and resources from having to train multiple machine learning models from scrape to complete similar tasks.
2. The process of training models in machine learning high amount of resources and transfer learning makes the process more efficient.
3. To balance a lack of labeled training data by using pre-trained models.
When to Use Transfer Learning?
Transfer learning can be used when developers don’t have labeled data to train a new model or don't have an extensive time frame to construct a new model from scratch. Training new models require a lot of accurate data. And building a new model from scratch is a lot of man work as developers need to label endless datasets.
In such cases, data scientists can use transfer learning and export models or data layers as per the requirement. Considering that you had a huge amount of dataset, you might still not have the resources sufficient to train a model on such a large dataset. Hence transfer learning makes a lot of sense if you don’t have the compute resources needed to train models on huge datasets.
At times data scientists might not have sufficient data to train their machine learning models. Working with an insufficient amount of data would result in lower performance, starting with a pre-trained model would help data scientists build better models.
What are transfer learning techniques/algorithms?
There are a few methods/techniques to carry out the process of transfer learning in machine learning and deep learning. These techniques help data sciences to connect or reuse existing models for training new models in neural network training. Following are three different categories of transfer learning algorithms used in ML.
1. Inductive Transfer learning:
In the inductive transfer learning technique, the source and target are the same, but the target and source task differ from each other. The algorithm works on the assumption/inductive biases of the source domain to help improve the target task. The functionality of the algorithm doesn't depend on the fact whether the source domain contains labeled data or not.
2. Unsupervised Transfer Learning:
In this technique, data scientist focuses on unsupervised tasks in the target domain that needs to be reused for transfer learning. The source and target domains are similar, but their tasks and functionalities are different. In this scenario, labeled data is unavailable in either of the domains.
3. Transductive Transfer Learning:
In this method, there are similarities between the source and target tasks, but the respective domains are different. In this scenario, the source domain has a lot of labeled data, while the target domain has none. This can be further classified into subcategories, referring to settings where either the feature spaces are different or the marginal probabilities.
What are some example applications of transfer learning?
1. Image Recognition:
Transfer learning is going to bring a big difference to image recognition technology and leverage mobile devices. For example, a model used for identifying males can be used for identifying females.
2. Autonomous/ Automatic Driving:
A model train for Cars can be used for heavy-duty vehicles also. We can train these models to recognize elements on the road or train navigations.
3. Natural Language Processing (NLP):
Transfer learning can be used in NLP to train models where the knowledge of pre-trained AI models that can understand linguistic structures can be transferred to other models that aim to predict the next word in a series based on previous sentences.
Game developers can use the strategy data set for similar sorts of games or while developing an advanced version of them.
5. Speech Recognition:
An AI model developed for a specific language can be used for multiple languages with adequate training.