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CNN - Convolutional Neural Networks Data Augmentation Techniques Questions and Answers Flashcards

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  1. What is CutMix augmentation and how does it differ from Mixup?

    Answer: CutMix pastes a cut-out patch from one image onto another, mixing labels proportionally to the patch area

    CutMix replaces a rectangular region in one image with the corresponding region from another and mixes labels proportionally to the replaced area, unlike Mixup which blends entire images.

  2. What is mosaic augmentation commonly used for in object detection CNN training?

    Answer: Combining four images into a single training image to expose the model to multiple contexts simultaneously

    Mosaic augmentation stitches four training images into a 2×2 grid, allowing the detector to encounter objects at different scales and positions within a single forward pass.

  3. What is GridDistortion augmentation used for in CNN training?

    Answer: Applying elastic deformation by displacing image grid points to simulate geometric variability

    GridDistortion applies non-linear geometric deformations by shifting the intersection points of a grid, helping models become invariant to slight spatial distortions.

  4. What is the purpose of random rotation augmentation in CNN training?

    Answer: To make the model invariant to the orientation of objects in the image

    Random rotation augmentation rotates training images by a random angle, teaching the CNN to recognize objects regardless of their orientation.

  5. In CNN training, what does the term 'augmentation policy' refer to?

    Answer: A defined set of transformation operations along with their probabilities and magnitudes

    An augmentation policy specifies which transformations to apply, with what probability each is chosen, and at what intensity level, forming a complete augmentation strategy.

  6. What is the effect of applying Gaussian blur augmentation to CNN training images?

    Answer: It simulates out-of-focus conditions, making the model robust to image blurriness

    Gaussian blur augmentation convolves the image with a Gaussian kernel, simulating defocus or motion blur and helping CNNs generalize to lower-quality input images.

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