The vanilla Generative Adversarial Networks (GANs) are commonly used to generate realistic images depicting aged and rejuvenated faces. However, the performance of such vanilla GANs in the age-oriented face synthesis task is often compromised by the mode collapse issue, which may produce poorly synthesized faces with indistinguishable visual variations. In addition, recent age-oriented face synthesis methods use the L1 or L2 constraint to preserve the identity information in synthesized faces, which implicitly limits the identity permanence capabilities when these constraints are associated with a trivial weighting factor. In this paper, we propose a method for the age-oriented face synthesis task that achieves high synthesis accuracy with strong identity permanence capabilities. Specifically, to achieve high synthesis accuracy, our method tackles the mode collapse issue with a novel Conditional Discriminator Pool, which consists of multiple discriminators, each targeting one particular age category. To achieve strong identity permanence capabilities, our method uses a novel Adversarial Triplet loss. This loss, which is based on the Triplet loss, adds a ranking operation to further pull the positive embedding towards the anchor embedding to significantly reduce intra-class variances in the feature space. Through extensive experiments, we show that our proposed method outperforms state-of-the-art methods in terms of synthesis accuracy and identity permanence capabilities, both qualitatively and quantitatively.

中文翻译：

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具有条件鉴别器池和对抗性三元组损失的面向年龄的人脸合成


普通的生成对抗网络（GAN）通常用于生成描绘衰老和恢复活力的面孔的逼真图像。然而，这种普通 GAN 在面向年龄的人脸合成任务中的性能常常受到模式崩溃问题的影响，这可能会产生具有难以区分的视觉变化的合成不良的人脸。此外，最近的面向年龄的人脸合成方法使用 L1 或 L2 约束来保留合成人脸中的身份信息，当这些约束与琐碎的权重因子相关联时，这隐式地限制了身份持久性能力。在本文中，我们提出了一种面向年龄的人脸合成任务的方法，该方法实现了高合成精度和强大的身份持久性能力。具体来说，为了实现高合成精度，我们的方法使用一种新颖的条件鉴别器池来解决模式崩溃问题，该池由多个鉴别器组成，每个鉴别器针对一个特定的年龄类别。为了实现强大的身份持久性能力，我们的方法使用了一种新颖的对抗性三元组损失。这种损失基于 Triplet 损失，增加了排序操作，以进一步将正嵌入拉向锚嵌入，从而显着减少特征空间中的类内方差。通过大量的实验，我们表明，我们提出的方法在合成准确性和身份持久性能力方面，无论是定性还是定量，都优于最先进的方法。