K-shot

K-shot refers to a machine learning paradigm where models are trained or evaluated using exactly K examples per class or task, representing a specific instance of few-shot learning that constrains the number of training samples to a precise quantity. This approach tests a model’s ability to generalize from extremely limited data by providing exactly K labeled examples for each category, class, or task variant during training or inference. K-shot learning encompasses various scenarios including one-shot (K=1), two-shot (K=2), and higher K values, each presenting different challenges for model generalization and adaptation. Modern implementations utilize meta-learning algorithms, prototypical networks, and model-agnostic meta-learning (MAML) techniques to enable rapid adaptation to new tasks with precisely K examples per class.

Enterprise applications leverage K-shot learning for domain adaptation, personalization systems, and rapid deployment of AI solutions to new business contexts where collecting extensive training data is impractical or expensive. Advanced K-shot methods incorporate support-query set architectures, episodic training protocols, and gradient-based optimization to maximize learning efficiency from limited examples. This paradigm enables organizations to deploy AI systems that can quickly adapt to new scenarios, products, or customer segments with minimal data requirements while maintaining performance comparable to extensively trained models.