Are you wondering when you should use transfer learning for a machine learning project? Or maybe you are wondering what type of data is required in order to use transfer learning? Well either way, you are in the right place! In this article, we tell you everything you need to know to understand when you should and should not use transfer learning for a machine learning project.
We will start out by discussing what transfer learning is and what types of situations transfer learning is used in. After that, we will discuss what type of data is required in order to be able to apply transfer learning. Next, we will discuss some of the main advantages and disadvantages of transfer learning. Finally, we will provide examples of situations where it does and does not make sense to use transfer learning.
What is transfer learning?
What is transfer learning? Transfer learning is a technique that can be used to take a large model that has been trained for one task and repurpose the model to be used for a different task. This is done by taking a model that has already been trained on a large body of labeled data and continuing to train it using a new dataset. In this way, some (or all) of the model parameters are updated to adapt the model to the new task. The main difference between transfer learning and something like semi supervised learning is that transfer learning typically takes models that have been trained on a supervised task then trains them on a new task, whereas semi supervised learning often uses a pre-trained model that has been trained in an unsupervised fashion.
Transfer learning is primarily used in situations where you want to take a large deep learning model with many parameters and apply it to a specific dataset. If is most common for transfer learning to be used when dealing with text, image, video, or audio data. That is because models that are trained for this type of data are generally large and have a lot of parameters that need to be tuned.
What data is needed for transfer learning?
What data is required in order to implement transfer learning? Assuming that you are starting with a model that has been trained by someone else that you want to repurpose to be used in your use case, all you need in order to be able to implement transfer learning is a small set of labeled training data. Even if you are training a large model with a lot of parameters, you generally only need to have a few hundred records or a few thousand records rather than tens of thousands, hundreds of thousands, or even millions of records.
Advantages and disadvantages of transfer learning
What are some of the main advantages and disadvantages of transfer learning? In this section, we will discuss some of the main advantages and disadvantages of transfer learning. We will specifically focus on qualities that differentiate transfer learning from similar learning paradigms.
Advantages of transfer learning
Here are some of the main advantages of transfer learning.
- Requires less labeled data. One of the main advantages of transfer learning is that less labeled data is required in order to be able to train a model. This can be a huge advantage if you are operating in a situation where it is expensive or time consuming to generate labels for your training data.
- Fewer computational resources required. Another advantage of transfer learning is that since the model is not being trained for as long, there are generally fewer computational resources required. This can be an important factor if you are in a situation where large computational resources are not available.
- Cheaper model training. The fact that transfer learning requires less training time and fewer computational resources than training a model from scratch also means that it is generally cheaper to train a model using transfer learning. This can be a big advantage if you have limited monetary resources at your disposal.
- Faster model training. The same factors that impact the price of model training also impact the speed of model training. It is often much faster to train a model using transfer learning than it is to train a model from scratch.
- Improved model accuracy over a model trained on small data. Large models that are trained using transfer learning generally have better performance than models that are trained from scratch on smaller datasets.
Disadvantages of transfer learning
Here are some examples of disadvantages of transfer learning.
- Requires some labeled data. One disadvantage of transfer learning is that it does require some labeled data. This stands in contrast to techniques like self supervised learning that do not require any labeled data at all. If you are in a situation where it is difficult to obtain any labeled training data, then this could be a problem for you.
- Vocab can be an issue. If you are using transfer learning on a model that deals with text data, it is sometimes the case that the base model that you choose to continue training will have a fixed vocab that can not be updated to include terms that were not present during the initial model training. This can be a problem if you want to adapt a model to be used on a niche dataset that uses a highly technical vocabulary.
- May revert to the old model. There is a phenomenon in transfer learning known as negative transfer where the model reverts back to the initial task that it was trained on rather than adapting to the new task that the model has been retrained on. This most commonly happens when the new task that the model is being adapted to is very different from the initial task that the model was trained on.
- Cannot change model architecture. Since transfer learning takes a model that has already been specified and trained, that means that model architecture is generally already frozen. That means that you generally do not have as many levers to deal with if you encounter a problem like overfitting
When to use transfer learning
When should you use transfer learning to train a machine learning model? Here are some examples of situations where you should use transfer learning to train a machine learning model.
- When you have a small amount of labeled data, but want to train a large model. The main situation where you should use transfer learning is in situations where you have only a small amount of labeled data, but you need to train a large model with many parameters. This generally happens when you are training a model that performs a task on image, text, video, or audio data.
- When you need to train a large model fast and for cheap. Sometimes it makes sense to use transfer learning even when you have an unlimited amount of data available. That is because it can be slow and expensive to train a large model on a large amount of data. If you want to train a model quickly without expending too many monetary resources, then it may make sense to use transfer learning.
When not to use transfer learning
What are some examples of situations where it does not make sense to use transfer learning? Here are some examples of situations where it might not make sense to use transfer learning.
- When getting any amount of labeled data is difficult. If you are in a situation where getting any amount of labeled data for your task is difficult, then you may not be in a situation where it makes sense to use transfer learning. In these situations, it may make sense to use a paradigm like self supervised learning or unsupervised learning that does not require labeled data at all.
- When labeled data is easy to obtain and compute is readily available. If you are in a situation where labeled training data is easy to obtain and there are large computational resources available to you, then you may not stand to benefit as much from transfer learning. The main advantage of transfer learning is that it reduces the amount of data and compute resources required to train a model. If these are not pain points, then it may make sense for you to train a model from scratch.
- Common model training paradigms in machine learning
- When to use self supervised learning
- When to use semi supervised learning
- When to use active learning
- When to use weakly supervised learning