Title: Effective Abstract Reasoning with Dual-Contrast Network. Abstract: As a step towards improving the abstract reasoning capability of machines, we aim to solve Raven’s Progressive Matrices (RPM) with neural networks, since solving RPM puzzles is highly correlated with human intelligence. Unlike previous methods that use auxiliary annotations or assume hidden rules to produce appropriate feature representation, we only use the ground truth answer of each question for model learning,  aiming for an intelligent agent to have a strong learning capability with a small amount of supervision.  Based on the RPM problem formulation,  the correct answer filled into the missing entry of the third row/column has  to  best  satisfy  the  same  rules  shared  between  the  first  two  rows/columns.Thus  we  design  a  simple  yet  effective  Dual-Contrast  Network  (DCNet)  to  exploit the inherent structure of RPM puzzles.  Specifically, a rule contrast module is  designed  to  compare  the  latent  rules  between  the  filled  row/column  and  the first two rows/columns; a choice contrast module is designed to increase the relative differences between candidate choices.  Experimental results on the RAVEN and  PGM  datasets  show  that  DCNet  outperforms  the  state-of-the-art  methods by a large margin of 5.77%.   Further experiments on few training samples and model generalization also show the effectiveness of DCNet.  Code is available at https://github.com/visiontao/dcnet.