Which variation of GANs uses two discriminators: one as a typical binary classifier and the other as a score function?

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BlackTom AI · Accepted Answer

Before evaluating the choices, let's recall the core idea: in this variation of GANs, the architecture employs two discriminators with distinct roles to guide the generator.
Option 1: QuantGAN. This would typically imply a model designed to handle quantitative measurements or to quantify uncertainty, but the hallmark two-discriminator setup with a separate score function is not standardly associated with QuantGAN. Therefore, this option seems mismatched to the described architecture.
Option 2: regGAN. This choice matches the description because regGAN is described as using two discriminators: one functioning as a conventional binary classifier to distinguish real vs. fake samples, and another acting as a score function to regulate the generator through a separate objective. This aligns with a two-pronged defender setup where one discriminator provides the typical adversarial signal and the other supplies a continuous score for regularization or guidance.
Option 3: TAGAN. TAGAN is typically known for targeted or conditional adversarial setups, especially in contexts like text-to-image or attribute-conditioned generation, but it is not characteristically defined by a dual-discriminator arrangement that includes a dedicated score function. Hence, this option does not fit the described mechanism.
In summary, you can weigh each option by checking whether the described two-discriminator structure with a separate score function is a defining feature. The first option lacks that specific two-discriminator interpretation, the second explicitly states the dual-discriminator design with a scoring role, and the third does not inherently imply this architecture. The reasoning for each option reflects how well it matches the given architectural description.

Learning AI Through Visualization 4 Module 6 Quiz

Which variation of GANs uses two discriminators: one as a typical binary classifier and the other as a score function?

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