Zeroshot learning

Zeroshot learning is a machine learning paradigm where models perform classification or prediction tasks on categories they have never encountered during training, leveraging semantic knowledge and learned representations to generalize beyond their training distribution. This approach enables immediate adaptation to unseen classes by exploiting attribute-based learning, semantic embeddings, and cross-modal knowledge transfer between different data modalities. Zeroshot learning employs techniques like mapping textual descriptions to visual features, utilizing pre-trained word embeddings, and exploiting compositional understanding of concepts. Common implementations include computer vision models classifying novel object categories, natural language systems handling new domains, and recommendation engines processing previously unseen items. The capability emerges from models learning transferable patterns and relationships during training. For AI agents, zeroshot learning provides rapid deployment capabilities, cost-effective scaling across domains, and handling of unexpected scenarios.