论文标题
深度学习的双重体现符号概念表示
Dual Embodied-Symbolic Concept Representations for Deep Learning
论文作者
论文摘要
在认知神经科学的最新发现中，我们主张使用双级模型来概念表示：体现级别由面向概念的特征表示组成，符号级别由概念图组成。体现的概念表示是特定于模态的，并以特征空间中特征向量的形式存在。另一方面，符号概念表示是Amodal和语言特定的，并且以概念 /知识空间中的单词 /知识图形嵌入形式存在。人类概念系统既包括体现的表示形式，又包括符号表示，通常相互作用以驱动概念处理。因此，我们进一步倡导使用双重体现的符号概念表示来进行深度学习。为了证明它们的使用和价值，我们讨论了两个重要用例：用于几个类增量学习的体现符号知识蒸馏，以及用于图像文本匹配的体现符号融合的表示。双重体现符号概念表示是深度学习和符号AI整合的基础。我们讨论了此类集成的两个重要示例：具有知识图桥接的场景图生成和多模式知识图。
Motivated by recent findings from cognitive neural science, we advocate the use of a dual-level model for concept representations: the embodied level consists of concept-oriented feature representations, and the symbolic level consists of concept graphs. Embodied concept representations are modality specific and exist in the form of feature vectors in a feature space. Symbolic concept representations, on the other hand, are amodal and language specific, and exist in the form of word / knowledge-graph embeddings in a concept / knowledge space. The human conceptual system comprises both embodied representations and symbolic representations, which typically interact to drive conceptual processing. As such, we further advocate the use of dual embodied-symbolic concept representations for deep learning. To demonstrate their usage and value, we discuss two important use cases: embodied-symbolic knowledge distillation for few-shot class incremental learning, and embodied-symbolic fused representation for image-text matching. Dual embodied-symbolic concept representations are the foundation for deep learning and symbolic AI integration. We discuss two important examples of such integration: scene graph generation with knowledge graph bridging, and multimodal knowledge graphs.
