What is Transfer Learning?
Transfer learning is a machine learning technique in which a model created for one job is utilized as the basis for a model on a different task. Given the vast compute and time resources required to develop neural network models, it is a popular strategy in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks. In machine learning, transfer learning refers to the reuse of a previously trained model on a new issue.While in transfer learning, a machine uses knowledge from a previous assignment to improve prediction about a new task. When training a classifier to predict whether an image contains food, you can easily utilise the knowledge gathered during training to distinguish beverages.
During transfer learning, the information of an already trained machine learning model is transferred to a separate but closely related task. You might leverage the model’s training knowledge to detect other things such as sunglasses if you trained a simple classifier to predict whether an image contains a backpack.
When Should You Apply Transfer Learning?
When there isn’t enough annotated data with which to train our model. When a pre-trained model has been trained on data and tasks that are similar. You may easily recover the original model and retrain some layers for your job if you used TensorFlow to train it. Transfer learning, on the other hand, only works if the features learned in the first task are generalizable to other activities. In addition, the input to the model must be the same size as when it was first trained.
1. Training a model to reuse it
Consider the case when you want to complete task A but don’t have enough data to train a deep neural network. One way to get around this is to find a comparable task B with a lot of data. You may use the deep neural network to train on task B before applying the model to task A. Whether you need to use the complete model or just a few levels depends on the problem you’re trying to address. You can also reapply the model and create predictions for your fresh input if the input in both tasks is the same.
2. Using a pre-trained model
The second alternative is to use a model that has been already trained. There are plenty of these models available, so do some research beforehand. The task determines the number of layers to reuse and retrain. Keras is made up of nine pre-trained models that may be used for transfer learning, prediction, and fine-tuning. You can find these models, as well as some quick lessons on how to use them, here. Many research institutions now provide access to trained models. Deep learning is the most popular use of this type of transfer learning.
3. Extraction of features
Another alternative is to use deep learning to find the best representation of your problem, which entails determining the most important aspects. This technique is known as representation learning, and it can often outperform hand-drawn representations. In machine learning, feature development is mostly done by hand by researchers and domain experts. Fortunately, deep learning can extract features automatically. Of course, feature engineering and domain knowledge are still important; you must still determine which features to add to your network. Neural networks, on the other hand, can learn which characteristics are important and which aren’t. A representation learning algorithm can identify a good combination of traits in a short amount of time, even for intricate jobs that would normally need a lot of human work. After then, the learnt representation may be used to solve a number of additional problems. Simply utilise the early layers to identify the best feature representation, but don’t use the network’s output because it’s too task-specific. Send data into your network and out through one of the intermediary levels instead. As a result, the raw data may be interpreted as a representation of this layer. This strategy is popular in computer vision because it allows you to reduce the size of your dataset, which reduces computing time and makes it more suitable for traditional methods.
How Can Transfer Learning Be Used?
The inductive transfer is a type of transfer learning used in deep learning. When the scope of potential models (model bias) is restricted in a good way by utilising a model fit on a separate but related job, the scope of possible models (model bias) is narrowed in a beneficial way. Transfer learning may be applied to your own predictive modelling challenges. The following are two frequent approaches:
1. Develop Model Approach
2. Pre-trained Model Approach
1. Develop Model Approach
a. By selecting the Source Task: You must choose a relevant predictive modelling issue with a large amount of data in which the input data, output data, and/or ideas learnt throughout the mapping from input to output data have some link.
b. Creating a source model: The next step is to create a skilled model for this initial assignment. To ensure that any feature learning has occurred, the model must be better than a raw model.
c. The Reuse Model: The model fit on the source job can then be utilised to build a model for the second task of interest. Depending on the modelling approach employed, this may include utilising all or sections of the model.
d. The Tune Model: On the input-output pair data available for the job of interest, the model may need to be altered or enhanced.
2. Pre-trained Model Approach
a. Select Source Model: From the available models, a pre-trained source model needs to be chosen. As many research institutions release models on huge and challenging datasets to choose the pool of candidate models.
b. Reuse Model: The pre-trained model can then be used as the starting point for a model on the second task of interest. This may involve using all or parts of the model, depending on the modeling technique used.
c. Tune Model. Optionally, the model may need to be adapted or refined on the input-output pair data available for the task of interest.
When to use Transfer Learning?
Transfer learning is an optimization, a shortcut to saving time or getting better performance. In general, it is not obvious that there will be a benefit to using transfer learning in the domain until after the model has been developed and evaluated.
What Are the Benefits of Transfer Learning?
Transfer learning has several advantages, the most prominent of which are as follows:
a. Shorter training times
b. Better neural network performance
c. Lack of a huge amount of data
A lot of data are normally required to train a neural network from start, but access to that data isn’t always feasible and this is when transfer learning comes in useful.
Transfer learning may build an effective machine learning model with very minimal training data since the model has previously been pre-trained. This is particularly beneficial in natural language processing, where large tagged datasets need a great deal of expertise. Furthermore, because training a deep neural network from the beginning of a challenging job might take days or even weeks, training time is reduced.