What does "Domain Gap" mean?
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Domain gap refers to the difference between two sets of data that come from different sources or environments. For example, if you have images generated by a computer (synthetic data) and real images taken from the world, there might be differences in how they look, feel, or behave. This can make it hard for computer models to work well when they are trained on one type of data but tested on another.
Importance of Addressing Domain Gap
When training models, especially in fields like machine learning or computer vision, it is important to reduce the domain gap. If the model is trained on synthetic images, it might not recognize items the same way when looking at actual images. Closing this gap can improve the model's ability to perform accurately in real-life situations.
Strategies to Reduce Domain Gap
To tackle the domain gap, researchers use various methods. Some techniques involve mixing synthetic data with real data, while others adjust how the model learns from the data. These strategies aim to help the model adapt better to the real world even if it was mostly trained on synthetic data.
Benefits of Reducing Domain Gap
By working to minimize the domain gap, models can achieve better performance in real-world tasks. This can lead to more accurate results and less reliance on expensive and time-consuming data collection processes. Overall, addressing the domain gap is essential for developing effective and efficient machine learning models.