{"ID":2891446,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17684","arxiv_id":"2507.17684","title":"Generalized Dual Discriminator GANs","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 $α$-GANs (D2 $α$-GANs), which combines the strengths of dual discriminators with the flexibility of a tunable loss function, $α$-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.","short_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...","url_abs":"https://arxiv.org/abs/2507.17684","url_pdf":"https://arxiv.org/pdf/2507.17684v1","authors":"[\"Penukonda Naga Chandana\",\"Tejas Srivastava\",\"Gowtham R. Kurri\",\"V. Lalitha\"]","published":"2025-07-23T16:46:03Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.IT\",\"stat.ML\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}