Data Augmentation in Deep Learning
10 Questions
6 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to lesson

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

What is the purpose of data augmentation?

  • To reduce the diversity of the training set
  • To decrease the size of the training set
  • To generate synthetic data from scratch
  • To prevent models from overfitting (correct)
  • What distinguishes augmented data from synthetic data?

  • Augmented data is generated artificially without using the original dataset
  • Synthetic data is generated by making minor changes to the original dataset
  • Augmented data is derived from original data with minor changes (correct)
  • Synthetic data is derived from original data with minor changes
  • What are examples of geometric transformations in image augmentation?

  • Randomly change RGB color channels, contrast, and brightness
  • Delete some part of the initial image
  • Randomly change the sharpness or blurring of the image
  • Randomly flip, crop, rotate, stretch, and zoom images (correct)
  • What does color space transformation involve in image augmentation?

    <p>Randomly change RGB color channels, contrast, and brightness</p> Signup and view all the answers

    When should data augmentation be used?

    <p>To prevent models from overfitting</p> Signup and view all the answers

    Which term best describes the process of 'automated measurement of physiological and/or behavioral characteristics to determine or authenticate identity'?

    <p>Biometric</p> Signup and view all the answers

    What is the primary difference between identification and authentication in biometric systems?

    <p>Many-to-one mapping vs. one-to-one mapping</p> Signup and view all the answers

    What is the meaning of 'unimodal' in the context of biometric systems?

    <p>Single mode of biometric authentication</p> Signup and view all the answers

    In biometric systems, what does 'automated measurement' primarily indicate?

    <p>No human involvement in measurement</p> Signup and view all the answers

    What is the main advantage of verification systems over identification systems in biometrics?

    <p>More accurate</p> Signup and view all the answers

    Study Notes

    Data Augmentation

    • Data augmentation is a technique used to increase the size of a dataset by applying transformations to existing data, thereby reducing overfitting and improving model performance.
    • Augmented data is distinct from synthetic data, which is entirely generated data that does not exist in the original dataset.

    Image Augmentation

    • Geometric transformations in image augmentation include:
      • Rotation
      • Scaling
      • Translation
      • Flipping
    • Color space transformation involves converting images between different color spaces (e.g., RGB to grayscale) to simulate varying lighting conditions or sensor responses.

    Biometrics

    • Biometric systems use automated measurement of physiological and/or behavioral characteristics to determine or authenticate identity.
    • Identification involves determining an individual's identity from a dataset, whereas authentication verifies an individual's claimed identity.
    • In biometric systems, 'unimodal' refers to the use of a single biometric trait (e.g., face recognition) for identification or authentication.
    • 'Automated measurement' primarily indicates the use of sensors or cameras to capture biometric data.
    • Verification systems, which authenticate a claimed identity, have the advantage of being more efficient and accurate than identification systems, which require searching the entire database to determine an individual's identity.

    Studying That Suits You

    Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

    Quiz Team

    Description

    Test your knowledge of data augmentation in deep learning with this quiz! Explore the concept of creating modified copies of a dataset using existing data and understand the difference between augmented and synthetic data. Perfect for deep learning enthusiasts and data scientists.

    More Like This

    Biometric Authentication Methods Quiz
    5 questions
    Biometric Systems Components Quiz
    10 questions
    Biometric Systems Quiz
    40 questions
    Biometric Systems Overview
    8 questions
    Use Quizgecko on...
    Browser
    Browser