Why were GANs designed to avoid using Markov chains?

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BlackTom AI · Accepted Answer

Consider the question: Why were GANs designed to avoid using Markov chains?
Option 1: 'Markov chains are too simple to capture complex data distributions.' This is not the primary limitation cited for Markov chains in the GAN context. Markov chains can, in theory, model complex distributions given enough states and steps, but in practice they become impractical due to other issues such as slow mixing and tractable likelihood concerns. So calling them simply 'too simple' mischaracterizes the core motivation.
Option 2: 'Markov chains have high computational costs.' This matches the widely cited reason: Markov chains often require expensive sampling to generate realistic data, and achieving good mixing and coverage of the data distribution can be computationally intensive. GANs seek a faster, more scalable training signal, so avoiding heavy Markov chain sampling is advantageous.
Option 3: 'GANs require labeled data, whereas Markov chains do not.' This is inaccurate as a general statement about GANs. Many GAN formulations are unsupervised and do not require labels, whereas Markov chains are stochastic processes used for sequence modeling and do not inherently rely on labeled data. Misstating the data requirements of GANs does not reflect the main design rationale for avoiding Markov chains.
Thus, the most correct reason centers on computational practicality: Markov chains can impose high computational costs during training and sampling, which GANs aim to circumvent for efficiency and scalability.

Learning AI Through Visualization 4 Module 6 Quiz

Why were GANs designed to avoid using Markov chains?

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