Transfer Learning and the Importance of Datasets

Document created by David Piskula Employee on Jul 2, 2020Last modified by David Piskula Employee on Jul 31, 2020
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Transfer learning is one the most important techniques in machine learning. It gives machine learning models the ability to apply past experience to quickly and more accurately learn to solve new problems. This approach is most commonly used in natural language processing and image recognition. However, even with transfer learning, if you don't have the right dataset, you will not get very far.


This application note aims to explain transfer learning and the importance of datasets in deep learning. The first part of the AN goes through the theoretical background of both topics. The second part describes a use case example based on the application from AN12603. It shows how a dataset of handwritten digits can be collected to match the input style of the handwritten digit recognition application. Afterwards, it illustrates how transfer learning can be used with a model trained on the original MNIST dataset to retrain it on the smaller custom dataset collected in the use case.


In the end, the AN shows that although handwritten digit recognition is a simple task for neural networks, it can still benefit from transfer learning. Training a model from scratch is slower and yields worse accuracy, especially if a very small amount of examples is used for training.


Training accuracy comparison for 300 examples per digit. The graph shows that training with transfer learning achieves the best accuracy faster and gives slightly better results.


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