论文标题
生成的神经符号机
Generative Neurosymbolic Machines
论文作者
论文摘要
调和符号和分布式表示是一个至关重要的挑战，可以解决当前深度学习的局限性。最近，通过以生成对象为中心的表示模型实现了这一方向的显着进步。在学习一种识别模型的同时，以无监督的方式吸收了以对象为中心的符号表示，例如从原始图像中界定框，但没有这种模型可以根据博学的世界密度的结构提供生成模型的另一重要能力，即生成（采样）。在本文中，我们提出了生成的神经符号机器，该机器是一种生成模型，结合了分布式和符号表示的好处，以支持符号组件的结构化表示和基于密度的生成。这两个至关重要的特性是通过两层潜在层次结构实现的，其全局分布式潜在用于柔性密度建模和结构化的符号潜在图。为了增加该层次结构中的模型灵活性，我们还提出了structDraw先验。在实验中，我们表明，就结构准确性和图像生成质量而言，提出的模型明显优于先前的结构化表示模型以及最新的非结构化生成模型。我们的代码，数据集和受过训练的模型可在https://github.com/jindongjiang/gnm上找到
Reconciling symbolic and distributed representations is a crucial challenge that can potentially resolve the limitations of current deep learning. Remarkable advances in this direction have been achieved recently via generative object-centric representation models. While learning a recognition model that infers object-centric symbolic representations like bounding boxes from raw images in an unsupervised way, no such model can provide another important ability of a generative model, i.e., generating (sampling) according to the structure of learned world density. In this paper, we propose Generative Neurosymbolic Machines, a generative model that combines the benefits of distributed and symbolic representations to support both structured representations of symbolic components and density-based generation. These two crucial properties are achieved by a two-layer latent hierarchy with the global distributed latent for flexible density modeling and the structured symbolic latent map. To increase the model flexibility in this hierarchical structure, we also propose the StructDRAW prior. In experiments, we show that the proposed model significantly outperforms the previous structured representation models as well as the state-of-the-art non-structured generative models in terms of both structure accuracy and image generation quality. Our code, datasets, and trained models are available at https://github.com/JindongJiang/GNM
