A fascinating advantage of deep learning neural systems is that they can be reused in other related problems and transfer learning is a method of doing it. After reading this post, you’ll know what transfer learning for deep learning is:
* What is transfer learning.
* Methods of performing it.
* Major Applications.
WHAT IS TRANSFER LEARNING?
Transfer learning is a procedure where a model trained on one issue is utilized somehow on another related issue. In simpler terms it utilizes the knowledge gained while solving one problem and applies it to a different but similar/related problem.
For example: The knowledge gained while recognizing motorcycles can be utilized to an extent to recognize cars or other vehicles.
TRANSFER LEARNING METHODS
Commonly there are 2 approaches:
1. Developing model approach
*Selecting the source task: You should select a related modeling problem which has abundance of data available where one can spot some relationship between the input data, output data, and the concepts discovered during the mapping from input to output data.
*Developing the source model: After selecting the source task one should be able to develop a skillful model for the first undertaken task.
*Reusing the model. The model used on the source task can then be used for a model on the second task of interest. This might involve the usage of all or parts of the model, which depends on the modeling technique that will be used.
*Tuning the Model: If needed the model may have to be refined or adapted according to the input-output pair data available for the task of interest.
2. PRE-TRAINED MODEL APPROACH
*Selecting the source Model: A pre-trained source model is selected from the list of available models.
*Reusing the model: The model which was pre-trained can later be used as a model on the second task of interest. This can involve usage of all or some parts of the model, which depends on the modeling technique that will be used.
*Tuning the model: If needed the model may have to be refined or adapted according to the input-output pair data available for the task of interest.
The second type of transfer learning method is adapted commonly in deep learning.
*Image classification: The neural networks can be classified as experts in recognizing the objects in an image because they are trained on huge datasets, which is time-consuming. But transfer learning can be helpful here as it helps in reducing the time to train the model by pre-training the model by using ImageNet, which consists millions of images each from different categories. A solid example of Image Classification could be seen in the area of medical imaging, here the convolutional model is trained on the ImageNet to identify and solve kidney related problems in ultrasound images.
*Gaming: The adoption of Deep Learning has taken the gaming experience to a new level. With the implication of transfer learning, the tactics/tricks learned in one game can be used to play another game, which is not possible to do for the human brain.
*Learning through simulations: Simulation is known to be used for activating many advanced ML systems in the real world. Learning from a simulation and then applying the knowledge gained from it to the real world is an illustration of transfer learning which has proved to be very useful in saving time and building models with greater accuracy. Enabling learning from simulations has benefits which makes the data collection easy as objects can be easily analyzed.
Apart from the above mentioned applications there are several other applications of transfer learning like:
*Adapting to new domains
*Transferring knowledge across languages
*Zero shot translation
and much more.
Transfer learning has built in a new wave of learning in machines with the help of reusing algorithms and reusing the applied logic, which in turn speeds up the learning process. This also helps in the reduction in the capital investment and also reduces the time invested in training a model.