Methodological guideline for synthetic data generation domain gap characterization and mitigation

These methodological guidelines describe the different activities to implement in order to solve a Machine Learning problem in presence of potentially biased data: Tools and methods for measuring and detecting a large domain gap between two datasets. Tools and Methods for generating a fully annotated synthetic dataset. And tools and methods to correct dataset bias via domain adaptation techniques.