Exemplar Guided

Exemplar-guided learning leverages a small set of representative examples ("exemplars") to improve model performance, particularly in scenarios with limited data or noisy inputs. Current research focuses on applying this approach to diverse tasks, including image classification, object counting, and video segmentation, often employing deep learning architectures like vision transformers and autoencoders, sometimes enhanced with techniques such as spectral filtering or cycle consistency loss. This methodology offers significant advantages in areas like few-shot learning and weakly supervised settings, improving efficiency and accuracy while reducing the need for extensive labeled datasets, with applications spanning computer vision, medical image analysis, and speech processing.