Increasing the Robustness of Deep Learning Models for Object Segmentation: A Framework for Blending Automatically Annotated Real and Synthetic Data.

Recent problems in robotics can sometimes only be tackled using machine learning technologies, particularly those that utilize deep learning (DL) with transfer learning. Transfer learning takes advantage of pretrained models, which are later fine-tuned using smaller task-specific datasets. The fine-tuned models must be robust against changes in environmental factors such as illumination since, often, there is no guarantee for them to be constant.