End-to-End Refinement Guided by Pre-trained Prototypical Classifier
This work addresses the need for automated refinement of noisy experimental data in domains like materials discovery, offering a solution to reduce manual effort, though it appears incremental as it builds on existing classifier and refinement techniques.
The paper tackles the problem of refining imperfect input patterns, such as flawed X-ray diffraction data, by introducing an imitation refinement approach guided by a pre-trained classifier that uses simulated theoretical data, resulting in refined patterns that imitate ideal data with demonstrated effectiveness in digit and materials discovery tasks.
Many real-world tasks involve identifying patterns from data satisfying background or prior knowledge. In domains like materials discovery, due to the flaws and biases in raw experimental data, the identification of X-ray diffraction patterns (XRD) often requires a huge amount of manual work in finding refined phases that are similar to the ideal theoretical ones. Automatically refining the raw XRDs utilizing the simulated theoretical data is thus desirable. We propose imitation refinement, a novel approach to refine imperfect input patterns, guided by a pre-trained classifier incorporating prior knowledge from simulated theoretical data, such that the refined patterns imitate the ideal data. The classifier is trained on the ideal simulated data to classify patterns and learns an embedding space where each class is represented by a prototype. The refiner learns to refine the imperfect patterns with small modifications, such that their embeddings are closer to the corresponding prototypes. We show that the refiner can be trained in both supervised and unsupervised fashions. We further illustrate the effectiveness of the proposed approach both qualitatively and quantitatively in a digit refinement task and an X-ray diffraction pattern refinement task in materials discovery.