Deep Learning with Limited Data: A Synthetic Approach
Abstract
This report focuses on how synthetic data, created using simulation or generative models, can be used to address the deep learning data challenge. These techniques offer many advantages: 1) data can be created for rare cases that are difficult to observe in the real world; 2) data can be automatically labeled without errors; and 3) data can be created with little or no infringement on privacy and integrity. Synthetic data can be integrated into the deep learning process using techniques such as data augmentation or by mixing synthetic data with real-world data prior to training. This report, however, focuses mainly on the use of transfer learning techniques where knowledge gained while solving one problem is transferred to more efficiently solve another related problem.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2021
- Accession Number
- AD1160019
Entities
People
- Farzad Kamrani
- Harald Stiff
- Johan Sabel
- Linus J. Luotsinen
- Lukas Lundmark
- Viktor Sandstrom