Approaches for synthetic-to-real domain adaptation
In this section, you will learn the key approaches for synthetic-to-real domain adaptation. We will discuss the following methods:
- Domain randomization
- Adversarial domain adaptation
- Feature-based domain adaptation
Let’s start with one of the most commonly used approaches for domain adaptation.
Domain randomization
Domain randomization is a mechanism or procedure usually used to mitigate the domain gap problem and improve the performance of ML models on the target domain. This approach aims at randomizing the main properties and attributes of the training data or environment, such as simulators to increase the diversity of the scenarios the ML model is exposed to in the training stage. Thus, we can increase the robustness of the ML model for scenarios that it may encounter in the future. For more information, please refer to Domain Randomization for Transferring Deep Neural Networks from Simulation...
