论文标题
包容性GAN：改善生成模型中的数据和少数群体覆盖范围
Inclusive GAN: Improving Data and Minority Coverage in Generative Models
论文作者
论文摘要
生成的对抗网络（GAN）为产生逼真的图像带来了快速的进步。然而，在亚组中，其建模能力的公平分配受到了较少的关注，如果不受控制，可能会导致对代表性不足的少数群体的潜在偏见。在这项工作中，我们首先将少数群体包容性问题作为数据覆盖范围之一，然后建议通过将对抗性培训与重建生成协调来改善数据覆盖率。实验表明，我们的方法在可见数据和看不见的数据上的数据覆盖范围方面优于现有的最新方法。我们开发了一个扩展名，该扩展可以明确控制模型应确保包含并验证其有效性的少数族裔亚组，从而在整个数据集中的整体绩效中几乎没有妥协。代码，模型和补充视频可在GitHub上找到。
Generative Adversarial Networks (GANs) have brought about rapid progress towards generating photorealistic images. Yet the equitable allocation of their modeling capacity among subgroups has received less attention, which could lead to potential biases against underrepresented minorities if left uncontrolled. In this work, we first formalize the problem of minority inclusion as one of data coverage, and then propose to improve data coverage by harmonizing adversarial training with reconstructive generation. The experiments show that our method outperforms the existing state-of-the-art methods in terms of data coverage on both seen and unseen data. We develop an extension that allows explicit control over the minority subgroups that the model should ensure to include, and validate its effectiveness at little compromise from the overall performance on the entire dataset. Code, models, and supplemental videos are available at GitHub.
