A Deeper Look at Bongard Problems

Machine learning, especially deep learning, has been successfully applied to a wide array of computer vision classification tasks in recent years. Infamous for requiring massive amounts of data to perform well at image classification problems, deep learning has so far been unable to solve Bongard problems (BPs), a set of abstract visual reasoning tasks invented in the 1960s. Each BP can be seen as a supervised learning task, with few training samples (6 for positive and 6 for negative), and often requiring highly abstract features to learn well. Automatically solving Bongard problems directly from images remains an ambitious goal, with very little machine learning literature on the topic. In this paper, we discuss several special properties of BPs as well as what it means to solve a BP. Making use of an expanded set of BP-like tasks to allow for a more careful evaluation of automated solvers, we develop and benchmark a deep learning based approach to solve these problems. To encourage work on this interesting problem, we also make freely available a dataset of over 200 BPs (https://​github.​com/​XinyuYun/​bongard-problems).