Imitation Refinement

Many real-world tasks involve identifying patterns from data satisfying background and prior knowledge, for which the ground truth is not available, but ideal data can be obtained, for example using theoretical simulations. We propose a novel approach, imitation refinement, which refines imperfect patterns by imitating ideal patterns. The imperfect patterns are obtained for example using an unsupervised learner. Imitation refinement imitates ideal data by incorporating prior knowledge captured by a classifier trained on the ideal data: an imitation refiner applies small modifications to imperfect patterns, so that the classifier can identify them. In a sense, imitation refinement fits the data to the classifier, which complements the classical supervised learning task. We show that our imitation refinement approach outperforms existing methods in identifying crystal patterns from X-ray diffraction data in materials discovery. We also show the generality of our approach by illustrating its applicability to a computer vision task.