What does "Semantic Augmentation" mean?

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Semantic augmentation is a method used in machine learning to improve how computers understand and work with data. It involves adding extra information to existing data sets. This extra information helps machines learn better by providing context about what they are looking at.

For example, if a computer is trying to recognize different objects in images, semantic augmentation can add labels or descriptions about those objects. This makes it easier for the computer to identify and classify them accurately. By doing this, computers can learn from fewer examples and still perform well in new situations.

In robot manipulation, semantic augmentation helps robots learn how to handle various objects more effectively. It allows them to take advantage of existing information to become more skilled in different tasks without needing a vast amount of data. This makes the training process faster and more efficient, allowing robots to adapt to different environments and tasks better.