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

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  1. What is the primary purpose of data augmentation in CNN training?

    Answer: To artificially expand the training dataset and improve generalization

    Data augmentation artificially expands the training dataset by applying transformations, helping CNNs generalize better to unseen data.

  2. Which augmentation technique randomly flips an image along its vertical axis?

    Answer: Horizontal flip

    A horizontal flip mirrors an image left-to-right along the vertical axis, creating a natural augmentation for many image classification tasks.

  3. What does the 'random crop' augmentation technique do during CNN training?

    Answer: Crops a random sub-region of the image for training

    Random cropping extracts a randomly positioned sub-region of the input image, forcing the CNN to learn from different spatial locations.

  4. What is Mixup augmentation in CNN training?

    Answer: Creating synthetic samples by linearly interpolating between two training images and their labels

    Mixup blends two training images and their corresponding labels by a random ratio λ, encouraging the model to learn smoother decision boundaries.

  5. What does the CutOut augmentation technique do?

    Answer: Randomly masks out a square region of the input during training

    CutOut randomly zeroes out a square patch in the training image, forcing the network to use context from the entire image rather than relying on a single discriminative region.

  6. Which PyTorch library is most commonly used for applying data augmentation transforms to image datasets?

    Answer: torchvision.transforms

    torchvision.transforms provides a comprehensive set of image augmentation operations such as RandomHorizontalFlip, RandomCrop, and ColorJitter.

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