论文标题
通过合成结构域的适应来组装语义上的表示形式的预测生成模型
Assembling Semantically-Disentangled Representations for Predictive-Generative Models via Adaptation from Synthetic Domain
论文作者
论文摘要
深度神经网络可以形成输入数据的高级分层表示。各种研究人员表明，这些表示形式可用于实现各种有用的应用。但是，此类表示通常基于数据中的统计数据，并且可能不符合应用程序可能需要的语义表示。有条件的模型通常用于克服这一挑战，但是它们需要大量注释的数据集，这些数据集很难获得并且创建昂贵。在本文中，我们表明可以在基于物理的引擎的帮助下生成语义对齐的表示形式。这是通过创建具有脱钩属性的合成数据集，学习合成数据集的编码器，并从合成域增强规定的属性，并使用来自真实域中的属性来实现。结果表明，所提出的（合成vae-gan）方法可以在不依赖真实数据标签的情况下构建人脸属性的条件预测基因生成模型。
Deep neural networks can form high-level hierarchical representations of input data. Various researchers have demonstrated that these representations can be used to enable a variety of useful applications. However, such representations are typically based on the statistics within the data, and may not conform with the semantic representation that may be necessitated by the application. Conditional models are typically used to overcome this challenge, but they require large annotated datasets which are difficult to come by and costly to create. In this paper, we show that semantically-aligned representations can be generated instead with the help of a physics based engine. This is accomplished by creating a synthetic dataset with decoupled attributes, learning an encoder for the synthetic dataset, and augmenting prescribed attributes from the synthetic domain with attributes from the real domain. It is shown that the proposed (SYNTH-VAE-GAN) method can construct a conditional predictive-generative model of human face attributes without relying on real data labels.
