Few-shot segmentation of complex microstructures: Minimizing training images by maximizing representativeness and diversity

• Additively maximizing the representativeness and diversity of training images (AMRD) consistently outperforms random training image selection in low-data regimes. • AMRD can surpass fully supervised learning with less than 20% as many training images. • Ideal training micrographs contain both diverse and rare features.