Zero shot training

Zero shot training is a machine learning paradigm where models are trained to perform tasks on categories or domains they have never encountered during training, leveraging learned representations and transferable knowledge to generalize beyond their training distribution. This approach enables models to handle novel scenarios without requiring task-specific examples by utilizing semantic embeddings, attribute-based learning, and cross-modal knowledge transfer. The training process focuses on learning generalizable patterns and relationships that can be applied to unseen categories through compositional understanding and semantic reasoning. Common techniques include pre-training on diverse datasets, learning shared feature spaces, and utilizing auxiliary information like textual descriptions or ontologies. For AI agents, zero shot training provides immediate adaptation capabilities without retraining, enabling deployment across diverse domains and handling of unexpected scenarios cost-effectively.