2017/09/29 by Dokhyam Hoshen, Michael Werman, Hoshen, Dokhyam +1 · 2 voices · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Processing and 3D Reconstruction #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Neural Networks and Applications #cs.AI #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.1710.01692
openalex publication_date 2017/09/29 · arxiv published 2017/09/29 · arxiv updated 2017/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
IQ tests are an accepted method for assessing human intelligence. The tests consist of several parts that must be solved under a time constraint. Of all the tested abilities, pattern recognition has been found to have the highest correlation with general intelligence. This is primarily because pattern recognition is the ability to find order in a noisy environment, a necessary skill for intelligent agents. In this paper, we propose a convolutional neural network (CNN) model for solving geometric pattern recognition problems. The CNN receives as input multiple ordered input images and outputs the next image according to the pattern. Our CNN is able to solve problems involving rotation, reflection, color, size and shape patterns and score within the top 5% of human performance.