KNN Clip

KNN (k-Nearest Neighbors) methods are being extensively explored to address challenges in various machine learning tasks, particularly those involving large datasets or continual learning. Current research focuses on adapting KNN for applications like open-vocabulary segmentation, task-oriented parsing, and semi-supervised intent classification, often incorporating it within larger architectures or using it to improve pseudo-labeling strategies. These advancements aim to improve efficiency, reduce reliance on extensive training data, and enhance the robustness of models across diverse domains, leading to more adaptable and scalable machine learning solutions.