In recent years, convolutional neural networks have shown its strong power for stereo matching cost learning. Current approaches learn the parameters of their models from public datasets with ground truth disparity. However, due to the limitations of these datasets and the difficulty of collecting new stereo data, current methods fail in real-life cases. In this work, we present a framework for learning stereo matching cost without human supervision. Our method updates the network parameter in a iterative manner. It starts with randomly initialized network. Correct matchings are carefully picked and used as training data in each round. In the end, the networks converges to a stable state, which performs comparably with supervised methods on various benchmarks.