论文标题
为渐进矩阵智能测试生成正确的答案
Generating Correct Answers for Progressive Matrices Intelligence Tests
论文作者
论文摘要
Raven的渐进式矩阵是多项选择的智能测试，其中人们试图在$ 3 \ times 3 $抽象图像中完成缺失的位置。以前解决此测试的尝试仅集中在多种选择中选择正确的答案。在这项工作中，我们将重点放在给定网格的正确答案上，而没有看到选择，从定义上讲，这是更难的任务。提出的神经模型结合了生成模型中的多个进步，包括使用同一网络采用多个途径，使用沿两个途径的重新聚集技巧，使其编码兼容，变异损失的动态应用，以及与选择性的背部封播过程相结合的复杂感知损失。我们的算法不仅能够产生一组合理的答案，而且能够在多项选择测试中与最先进的方法具有竞争力。
Raven's Progressive Matrices are multiple-choice intelligence tests, where one tries to complete the missing location in a $3\times 3$ grid of abstract images. Previous attempts to address this test have focused solely on selecting the right answer out of the multiple choices. In this work, we focus, instead, on generating a correct answer given the grid, without seeing the choices, which is a harder task, by definition. The proposed neural model combines multiple advances in generative models, including employing multiple pathways through the same network, using the reparameterization trick along two pathways to make their encoding compatible, a dynamic application of variational losses, and a complex perceptual loss that is coupled with a selective backpropagation procedure. Our algorithm is able not only to generate a set of plausible answers, but also to be competitive to the state of the art methods in multiple-choice tests.
