A Systematic Literature Review on Transfer Learning for 3D-CNNs
Marco Klaiber, Daniel Sauter, Hermann Baumgartl, Ricardo Buettner
Abstract
The dependence of convolutional neural networks on large-scale datasets for training is no secret. This is even more problematic when using 3D-CNNs since sufficient 3D datasets for training are scarce and expensive. One possible solution is transfer learning. In this comparison, the state-of-the-art techniques for 3D-CNNs are analyzed and compared. Therefore, a literature search in the databases IEEEXplore DL, ScienceDirect, SpringerLink, and ACM is conducted. The results are compared using the criteria field of application, datasets, 3D-CNN architecture, transfer learning technique, hyperparameters, and final performance. This comparison provides a basis for future work to promote understanding and usage of transfer learning for 3D-CNNs.