Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner. In the real world, the salient attributes of unlabeled data can be imbalanced. However, most of existing unsupervised conditional GANs cannot cluster attributes of these data in their latent spaces properly because they assume uniform distributions of the attributes. To address this problem, we theoretically derive Stein latent optimization that provides reparameterizable gradient estimations of the latent distribution parameters assuming a Gaussian mixture prior in a continuous latent space. Structurally, we introduce an encoder network and novel unsupervised conditional contrastive loss to ensure that data generated from a single mixture component represent a single attribute. We confirm that the proposed method, named Stein Latent Optimization for GANs (SLOGAN), successfully learns balanced or imbalanced attributes and achieves state-of-the-art unsupervised conditional generation performance even in the absence of attribute information (e.g., the imbalance ratio). Moreover, we demonstrate that the attributes to be learned can be manipulated using a small amount of probe data.

翻译：具有集束潜在空间的生成对抗性网络( GANs) 能够以完全不受监督的方式产生有条件的生成。 在现实世界中, 未贴标签数据的显著特征可能不平衡。 但是, 大部分现有的未贴标签的有条件GANs 无法适当地将这些数据的属性集中到其潜在空间中, 因为他们承担了属性的统一分布 。 为了解决这个问题, 我们理论上得出Stein 潜伏优化, 提供假设高斯混合物在连续潜质空间之前的潜在分布参数的可重新测量的梯度估计值。 从结构上看, 我们引入了一个编码网络和新颖的未经监督的有条件对比损失, 以确保单个混合成分生成的数据代表一个单一属性。 我们确认, 拟议的方法, 名为 Stein Litetant Optimination for GANs (SLOGAN), 成功地学习了平衡或不平衡的属性, 并取得了即使在缺少属性信息的情况下, 也能够实现最先进的、 不受监督的有条件的生成性能。 此外, 我们证明, 要学习的属性可以使用少量的探测数据加以操纵。