Inductive and Transductive Transfer learning : COMODEL
Aditya Paprunia, Jyoti Malhotra
Transfer Learning is a commonly used method in deep learning to train neural networks from a previously trained model. It focuses on using knowledge gained while solving one problem to solve a similar problem without explicitly developing a solution for the new problem. Similarly, previously trained models were created for a specific goal, but their learnings can be applied to other tasks as well. The new model uses the pre-trained model as the starting point for the new task. It extracts meaningful features from previous models and uses them for the current problem. In this research paper we compare the performance of the different pre-trained models; as per their accuracy with training data and validation data to get an idea of which model is best suited to give better results when transfer learning is used.
Article Details
Unique Paper ID: 155681

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 1524 - 1531
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