Generalized Dual Discriminator GANs

• URL: http://arxiv.org/abs/2507.17684v1
• Date: Wed, 23 Jul 2025 16:46:03 GMT
• Title: Generalized Dual Discriminator GANs
• Authors: Penukonda Naga Chandana, Tejas Srivastava, Gowtham R. Kurri, V. Lalitha,
• Abstract summary: Dual discriminator generative adversarial networks (D2 GANs) were introduced to mitigate the problem of mode collapse in generative adversarial networks.<n>In this work, we first introduce dual discriminator $alpha$-GANs (D2 $alpha$-GANs), which combines the strengths of dual discriminators with the flexibility of a tunable loss function.
• Score: 5.604045325797645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dual discriminator generative adversarial networks (D2 GANs) were introduced to mitigate the problem of mode collapse in generative adversarial networks. In D2 GANs, two discriminators are employed alongside a generator: one discriminator rewards high scores for samples from the true data distribution, while the other favors samples from the generator. In this work, we first introduce dual discriminator $\alpha$-GANs (D2 $\alpha$-GANs), which combines the strengths of dual discriminators with the flexibility of a tunable loss function, $\alpha$-loss. We further generalize this approach to arbitrary functions defined on positive reals, leading to a broader class of models we refer to as generalized dual discriminator generative adversarial networks. For each of these proposed models, we provide theoretical analysis and show that the associated min-max optimization reduces to the minimization of a linear combination of an $f$-divergence and a reverse $f$-divergence. This generalizes the known simplification for D2-GANs, where the objective reduces to a linear combination of the KL-divergence and the reverse KL-divergence. Finally, we perform experiments on 2D synthetic data and use multiple performance metrics to capture various advantages of our GANs.

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