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2 cards from this section. Quick, good the night before a test.
What does GAN stand for?
Think about the role of both creator and evaluator in a network.
Which model generates images from text?
Focus on recent AI advancements in generating visuals from prompts.
What is the main goal of Generative Adversarial Networks (GANs)?
Think about the role of 'generative' in the name.
True or False: Diffusion models use a single step to generate images.
Consider the nature of refinement in their methodology.
Design an innovative application where GANs could be used beyond art and entertainment. Describe the concept.
Think about industries that need tailored outputs.
Critique the efficiency of diffusion models compared to GANs in terms of processing time and output quality.
Reflect on both the strengths and weaknesses of each model in practical use.
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