What is the impact of augmented data on AI models?
Augmented data enhances AI models by providing more diverse training examples, improving accuracy and robustness.
Can you give me an example of how augmented data is used?
Sure! In image recognition, augmented data can include variations like rotations, flips, or color changes to create more training images.
Does using augmented data always lead to better performance?
Not always. While it can improve performance, the quality of the augmented data and its relevance to the task are crucial.
What are some common techniques for data augmentation?
Common techniques include rotation, scaling, cropping, flipping, and adding noise to the original data.
How do I know if I should use augmented data for my model?
If your dataset is small or lacks diversity, using augmented data can be beneficial. It's best to experiment and validate.
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