论文标题
基于图的启发式搜索神经模块网络中的模块选择过程
Graph-based Heuristic Search for Module Selection Procedure in Neural Module Network
论文作者
论文摘要
神经模块网络（NMN）是一个机器学习模型，用于求解视觉问题回答任务。 NMN使用程序来编码模块的结构，其模块化体系结构使其能够更合理地解决逻辑问题。但是，由于模块选择的不可分割的过程，很难端到端训练NMN。为了克服这个问题，现有的工作要么将基础真相计划包括在培训数据中，要么应用强化学习以探索该计划。但是，这两种方法仍然存在弱点。考虑到这一点，我们为NMN提出了一个新的学习框架。基于图的启发式搜索是我们提出的算法，该算法是通过在名为程序图的数据结构上的启发式搜索来发现最佳程序。我们对FigureQA和CLEVR数据集的实验表明，我们的方法可以实现NMN的培训，而无需基础真相计划，并在程序探索中实现了比现有的强化学习方法的效率。
Neural Module Network (NMN) is a machine learning model for solving the visual question answering tasks. NMN uses programs to encode modules' structures, and its modularized architecture enables it to solve logical problems more reasonably. However, because of the non-differentiable procedure of module selection, NMN is hard to be trained end-to-end. To overcome this problem, existing work either included ground-truth program into training data or applied reinforcement learning to explore the program. However, both of these methods still have weaknesses. In consideration of this, we proposed a new learning framework for NMN. Graph-based Heuristic Search is the algorithm we proposed to discover the optimal program through a heuristic search on the data structure named Program Graph. Our experiments on FigureQA and CLEVR dataset show that our methods can realize the training of NMN without ground-truth programs and achieve superior efficiency over existing reinforcement learning methods in program exploration.
