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AI0-001 AI Concepts and Techniques Practice Question

A generative AI model produces images from text prompts. The outputs are often blurry and lack fine details. Which model type is MOST likely being used, and which improvement would best address this issue?

⚠ Common exam trap

The AI0-001 exam often tests the misconception that GANs are always the best for sharp images, but the trap here is that the question specifically describes blurry outputs—a hallmark of VAEs—and the best modern improvement is a diffusion model, not a GAN.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Variational Autoencoder (VAE); switch to a diffusion model

Variational Autoencoders (VAEs) are known for producing blurry outputs because their loss function (ELBO) encourages pixel-wise averaging, which smooths out fine details. Diffusion models, by contrast, iteratively denoise a random field, learning to reconstruct high-frequency details through a multi-step reverse process, directly addressing the blurriness issue.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Variational Autoencoder (VAE); switch to a diffusion model

    Why this is correct

    VAEs tend to blur; diffusion models iteratively denoise, producing high-quality details.

  • Variational Autoencoder (VAE); switch to a Generative Adversarial Network (GAN)

    Why it's wrong here

    GANs can produce sharp images but require careful training; diffusion models are more state-of-the-art for detail.

  • Generative Adversarial Network (GAN); increase the discriminator's capacity

    Why it's wrong here

    GANs typically produce sharp images, not blurry; blur suggests a VAE.

  • Diffusion model; use a larger batch size during training

    Why it's wrong here

    Diffusion models usually produce sharp outputs; blur suggests a VAE.

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