Abhishek Sharma, Practice Head for Engineering AI at Tech Mahindra, presents the “DNN Training Data: How to Know What You Need and How to Get It” tutorial at the May 2021 Embedded Vision Summit.
Successful training of deep neural networks requires the right amounts and types of annotated training data. Collecting, curating and labeling this data is typically one of the most time-consuming aspects of developing a deep-learning-based solution.
In this talk, Sharma discusses approaches useful for situations where insufficient data is available, including transfer learning and data augmentation, including the use of generative adversarial networks (GANs). He also discusses techniques that can be helpful when data is plentiful, such as transforms, data path optimization and approximate computing. He illustrates these techniques and challenges via case studies from the healthcare and manufacturing industries.
See here for a PDF of the slides.
