  Raven's Progressive Matrices (RPM) have been widely used for Intelligence
Quotient (IQ) test of humans. In this paper, we aim to solve RPM with neural
networks in both supervised and unsupervised manners. First, we investigate
strategies to reduce over-fitting in supervised learning. We suggest the use of
a neural network with deep layers and pre-training on large-scale datasets to
improve model generalization. Experiments on the RAVEN dataset show that the
overall accuracy of our supervised approach surpasses human-level performance.
Second, as an intelligent agent requires to automatically learn new skills to
solve new problems, we propose the first unsupervised method, Multilabel
Classification with Pseudo Target (MCPT), for RPM problems. Based on the design
of the pseudo target, MCPT converts the unsupervised learning problem to a
supervised task. Experiments show that MCPT doubles the testing accuracy of
random guessing e.g. 28.50% vs. 12.5%. Finally, we discuss the problem of
solving RPM with unsupervised and explainable strategies in the future.
