提出新型生成对抗网络，解决模式崩溃问题并提升生成质量。
Generalized Dual Discriminator GANs
- 引入双判别器与可调损失函数，增强模型灵活性
- 理论证明优化目标为 f-散度与反向 f-散度的线性组合
- 实验验证在合成数据上生成效果更优，适合生成任务研究者
双判别器生成对抗网络（D2 GAN）旨在缓解生成对抗网络中的模式崩溃问题。本文首先提出双判别器 α-GAN（D2 α-GAN），结合双判别器结构与可调损失函数 α-损失，提升模型灵活性。进一步将该方法推广至正实数上的任意函数，构建广义双判别器生成对抗网络。针对每种模型，我们提供理论分析，证明其极小极大优化等价于 f-散度与反向 f-散度的线性组合，推广了 D2-GAN 中目标函数简化为 KL 散度与反向 KL 散度线性组合的结果。最后，在二维合成数据上进行实验，并使用多种评估指标验证所提 GAN 的优势。
原文摘要 · Abstract (English)
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.
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