What is Transfer Learning?

Transfer learning describes a method in the field of Machine Learningwhich are mainly used for Classification tasks is applied. Transfer Learning is based on the approach that the Algorithm a pre-trained Model (e.g. of a artificial neural network) of a similar or also foreign use case and supplements or extends it with additional layers.which are aimed at the specific application.

The difference of transfer learning to other machine learning methods is that the Model not trained from scratch but can be based on data that has already been trained. The pre-trained model is normally adopted unchanged, which is why we also speak of "frozen layers". The trained model is then transferred to the model of the specific use case and subsequently concretised by adding further layers. This approach has the advantage of saving time and resources, as a certain part of the training can be skipped and a pre-trained model can be used.

The method is often used in the Object recognition and image data analysis Application. As a tangible example of the function of the approach of this learning method, the case is often mentioned that a pre-trained model can basically identify dogs in photos or distinguish them from other animals or objects. In the context of transfer learning, the classification layer is extended to include training for the ability to distinguish between different breeds of dogs. Something similar can also be applied in a previous step, in which models were trained to distinguish dogs from cats, for example, or living beings from objects in general.

What are applications and examples?

The areas of application of transfer learning are manifold, whereby the currently largest area of application in image data analysis lies. Since videos are merely a string of individual images, the method can clearly also be applied to videos. In order for machines to be able to "see" or understand the information on images, they must first be made readable for machines, which is the area of the Computer Vision is to be attributed.

A well-known model in the field of Image classification The Vision Transformer (ViT) which lays the foundation for further data processing. Another area of application is the Language analysisbut also the processing of text data. In the sub-area of the Natural Language Processing (NLP) human language is made understandable for machines, which lays the foundation for further applications of transfer learning. A very large and useful field of application is the Medical image classification or image analysis is the most important. Transfer learning is used above all in computer-assisted tissue recognition in connection with imaging examination methods such as CT or MRT. With the help of the learning method, abnormalities in tissue images can be identified and categorised or classified, which can indicate tumours, cancer or other diseases, for example.

Some Programme libraries and frameworks like Keras or PyTorch offer simple implementation possibilities of pre-trained models that are used as attachment points for transfer learning. In the field of image recognition, for example, ResNet from Microsoft or Inception from Google can be mentioned. These models are implemented in the library and then modified or extended within the framework of so-called "fine-tuning" so that the model can be used for the specific application.