论文标题
概念上的vae
The Conceptual VAE
论文作者
论文摘要
在本报告中，我们基于变异自动编码器的框架提出了一种新的概念模型，该模型旨在具有有吸引力的属性，例如概念域，同时可以从数据中学习。该模型的灵感来自概念的β-VAE模型，但被设计为与语言更紧密相关，因此概念的名称构成了图形模型的一部分。我们提供的证据表明，我们的模型（我们称为概念VAE）能够从彩色形状的简单图像以及相应的概念标签中学习可解释的概念表示形式。我们还展示了如何将模型用作概念分类器，以及如何将其用于每个实例的标签中学习。最后，我们将我们的模型正式与Gardenfors的概念空间理论联系起来，展示了我们用来代表概念的高斯人如何根据这样一个空间中的“模糊概念”形式化。
In this report we present a new model of concepts, based on the framework of variational autoencoders, which is designed to have attractive properties such as factored conceptual domains, and at the same time be learnable from data. The model is inspired by, and closely related to, the Beta-VAE model of concepts, but is designed to be more closely connected with language, so that the names of concepts form part of the graphical model. We provide evidence that our model -- which we call the Conceptual VAE -- is able to learn interpretable conceptual representations from simple images of coloured shapes together with the corresponding concept labels. We also show how the model can be used as a concept classifier, and how it can be adapted to learn from fewer labels per instance. Finally, we formally relate our model to Gardenfors' theory of conceptual spaces, showing how the Gaussians we use to represent concepts can be formalised in terms of "fuzzy concepts" in such a space.
