The technique where spatial transformations are applied to input images to boost the performance and versatility of models is called _______ in computer vision.

  • Edge Detection
  • Data Augmentation
  • Optical Flow
  • Feature Extraction
Data augmentation involves applying spatial transformations to input images, such as rotation, flipping, or cropping, to increase the diversity of the training data. This technique enhances model generalization and performance.
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