Synthetic Data for Computer Vision Problems, When Real Annotation Is Too Expensive
IDeep learning has been successfully used for various computer vision problems. However, models often need large amounts of data to solve a specific task effectively. Unfortunately, there is often a bottleneck to accessing labeled data. With the advancement in graphic tools, simulator engines, and generative models, synthetic data generation has become a suitable alternative to approach the lack of data in specific domains.
We will talk about the current development in synthetic data generation and how we have used it to build good models for computer vision tasks based primarily on synthetic data. We will go through the different use cases, explain the tools used and give an insight into how we approach a computer vision problem when we lack a large amount of labeled data.
Abgeiba is a machine learning (ML) engineer at Modulai, an ML consultancy firm in Sweden. As part of her role as an ML engineer, she has worked on various projects applying and developing AI products for different industries. Prior to joining Modulai, she worked on ML projects at Ericsson and BBVA banking. Abgeiba holds an M.Sc. in Machine Learning from KTH, Sweden, and a B.Sc. in Electronics and Telecommunications from Tecnológico de Monterrey, Mexico.
