Generative Adversarial Networks (GANs) Lecture
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Questions and Answers

What is the primary function of the discriminator in the neural network architecture?

  • To distinguish between real and fake images, and provide feedback to the generator (correct)
  • To optimize the parameters of the generator
  • To generate realistic images from noise vectors
  • To sample images from the real image dataset
  • What is the distribution that the generator aims to sample from?

  • The distribution of fake images
  • The distribution of noise vectors
  • The distribution of real images in the dataset (correct)
  • A uniform distribution over the output space
  • What is the purpose of the generator in the neural network architecture?

  • To optimize the parameters of the discriminator
  • To sample images from the real image dataset
  • To generate realistic images from noise vectors (correct)
  • To classify real and fake images
  • What is the relationship between the generator and the discriminator in the neural network architecture?

    <p>The discriminator provides feedback to the generator</p> Signup and view all the answers

    What is the desired outcome of the generator in terms of the generated image G(z)?

    <p>G(z) is similar to a random image x in terms of distribution</p> Signup and view all the answers

    What is the primary difference between explicit generative models and implicit generative models?

    <p>The estimation of the probability distribution p of x</p> Signup and view all the answers

    What is the role of the neural network in GANs?

    <p>To apply complex transformations on the noise vectors</p> Signup and view all the answers

    What is the primary limitation of variational autoencoders compared to GANs?

    <p>VAEs require controlling the latent space</p> Signup and view all the answers

    What is the purpose of the random noise vectors in GANs?

    <p>To provide a random input to the neural network</p> Signup and view all the answers

    What is the key advantage of GANs compared to other generative models?

    <p>Ability to generate very good images without knowing the probability</p> Signup and view all the answers

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