K-shot learning

K-shot learning is a machine learning paradigm where models are trained or evaluated using exactly K labeled examples per class or task, representing a specific instance of few-shot learning that constrains the number of training samples to a precise quantity to test a model’s ability to generalize from extremely limited data. This approach provides exactly K labeled examples for each category, class, or task variant during training or inference phases, encompassing scenarios including one-shot (K=1), two-shot (K=2), five-shot (K=5), and higher K values that present different challenges for model generalization and adaptation capabilities. K-shot learning utilizes meta-learning algorithms, prototypical networks, model-agnostic meta-learning (MAML), and gradient-based optimization techniques that enable rapid adaptation to new tasks with precisely K examples per class while maintaining performance comparable to extensively trained models. Modern implementations incorporate support-query set architectures, episodic training protocols, and few-shot prompting strategies that maximize learning efficiency from limited examples through sophisticated representation learning and transfer mechanisms. Enterprise applications leverage K-shot learning for domain adaptation, personalization systems, rapid deployment of AI solutions to new business contexts, and scenarios where collecting extensive training data is impractical, expensive, or time-consuming. Advanced K-shot methods support compositional learning, multi-task scenarios, and dynamic adaptation that enable organizations to deploy AI systems capable of quickly adapting to new products, customer segments, or operational requirements with minimal data requirements.