1 research outputs found
Logic could be learned from images
Logic reasoning is a significant ability of human intelligence and also an
important task in artificial intelligence. The existing logic reasoning
methods, quite often, need to design some reasoning patterns beforehand. This
has led to an interesting question: can logic reasoning patterns be directly
learned from given data? The problem is termed as a data concept logic (DCL).
In this study, a learning logic task from images, just a LiLi task, first is
proposed. This task is to learn and reason the relation between two input
images and one output image, without presetting any reasoning patterns. As a
preliminary exploration, we design six LiLi data sets (Bitwise And, Bitwise Or,
Bitwise Xor, Addition, Subtraction and Multiplication), in which each image is
embedded with a n-digit number. It is worth noting that a learning model
beforehand does not know the meaning of the n-digit number embedded in images
and relation between the input images and the output image. In order to tackle
the task, in this work we use many typical neural network models and produce
fruitful results. However, these models have the poor performances on the
difficult logic task. For furthermore addressing this task, a novel network
framework called a divide and conquer model (DCM) by adding some prior
information is designed, achieving a high testing accuracy