The existing research work on facial age synthesis has been mostly focused on long-term aging (e.g., over an age span of 10 years or more). In this paper, we employ generative adversarial networks (GANs) as a tool to investigate age synthesis over different age spans. Compared with long-term aging, short-term age synthesis suffers from the reduced amount of available training data, which can severely hinder the model training. We conduct a series of experiments to validate this. To facilitate short-term age synthesis, we further propose label distribution-guided generative adversarial network (ldGAN), where each sample is associated with an age label distribution (ALD) rather than a single age group. Accordingly, each sample can contribute not only to the learning of its own age group but also to neighbouring groups' learning. This is useful when addressing short-term aging to cope with the reduced amount of training data. In addition, unlike one-hot encoding which treats age groups as independent from one another, ldGAN can well capture the correlation among different age groups, so that smooth aging sequences can be achieved. The ALD model is integrated into GAN with a two-step process. Firstly, instead of the traditional one-hot encoding, ALD is applied as the condition of the generator. Secondly, we add a sequence of label distribution learners on top of several multi-scale discriminators, with the aim of minimizing the label distribution learning loss when optimizing both the generator and discriminators. Both qualitative and quantitative evaluations are conducted to assess ldGAN's ability in dealing with two core issues of face aging, i.e., aging effect generation and identity preservation. The obtained experimental results demonstrate the effectiveness of ldGAN in both learning short-term aging patterns and coping with the lack of training data.

中文翻译：

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使用标签分布引导的生成对抗网络进行面部年龄合成


现有的面部年龄综合研究工作主要集中在长期衰老（例如，年龄跨度超过10年或更长）。在本文中，我们采用生成对抗网络（GAN）作为工具来研究不同年龄跨度的年龄综合。与长期老化相比，短期年龄合成受到可用训练数据量减少的影响，这会严重阻碍模型训练。我们进行了一系列实验来验证这一点。为了促进短期年龄合成，我们进一步提出了标签分布引导的生成对抗网络（ldGAN），其中每个样本与年龄标签分布（ALD）而不是单个年龄组相关联。因此，每个样本不仅可以为其自己年龄组的学习做出贡献，而且可以为邻近群体的学习做出贡献。这在解决短期老化问题以应对训练数据量减少时非常有用。此外，与将年龄组视为彼此独立的one-hot编码不同，ldGAN可以很好地捕获不同年龄组之间的相关性，从而可以实现平滑的老化序列。 ALD 模型通过两步过程集成到 GAN 中。首先，应用ALD作为生成器的条件，而不是传统的one-hot编码。其次，我们在几个多尺度判别器之上添加一系列标签分布学习器，目的是在优化生成器和判别器时最小化标签分布学习损失。通过定性和定量评估来评估ldGAN处理人脸老化的两个核心问题，即老化效应生成和身份保存的能力。 获得的实验结果证明了 ldGAN 在学习短期老化模式和应对训练数据缺乏方面的有效性。