论文标题
从抽象项目到潜在空间再到观察到的数据和背面：组成变分自动编码器
From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder
论文作者
论文摘要
现在，有条件的生成模型被认为是机器学习中的重要工具。本文着重于他们的控制。尽管许多方法旨在通过对其潜在表示的坐标控制来解散数据，但本文探讨了另一个方向。所提出的组合使用自然的多元结构（即自然可以分解为元素）处理数据。 Compvae源自贝叶斯变异原理，学习了一个潜在的表示，以利用观察性和符号信息。该方法的第一个贡献是，该潜在表示支持组成生成模型，该模型可与多元的操作（组成中元素的添加或减法）适合。整个框架W.R.T.的不变性和一般性可以实现这种组成能力。分别是元素的顺序和数量。本文的第二个贡献是关于合成1D和2D问题的概念证明，证明了拟议方法的效率。
Conditional Generative Models are now acknowledged an essential tool in Machine Learning. This paper focuses on their control. While many approaches aim at disentangling the data through the coordinate-wise control of their latent representations, another direction is explored in this paper. The proposed CompVAE handles data with a natural multi-ensemblist structure (i.e. that can naturally be decomposed into elements). Derived from Bayesian variational principles, CompVAE learns a latent representation leveraging both observational and symbolic information. A first contribution of the approach is that this latent representation supports a compositional generative model, amenable to multi-ensemblist operations (addition or subtraction of elements in the composition). This compositional ability is enabled by the invariance and generality of the whole framework w.r.t. respectively, the order and number of the elements. The second contribution of the paper is a proof of concept on synthetic 1D and 2D problems, demonstrating the efficiency of the proposed approach.
