Exemplar Image

Exemplar-based methods are transforming various machine learning tasks by leveraging carefully selected examples to improve model performance and explainability. Current research focuses on optimizing exemplar selection and integration within different model architectures, including transformers and diffusion models, for applications such as image inpainting, object counting, and class-incremental learning. This approach offers significant advantages in scenarios with limited data or a need for enhanced model interpretability, impacting fields ranging from scientific discovery to medical image analysis and software development. The efficiency and effectiveness of exemplar selection are key areas of ongoing investigation.