Zero-Shot Learning is a method that enables learning new categories without any samples. Typically, a model can only recognize the categories it has seen in the training set. However, through zero-shot learning, the model can leverage auxiliary information for reasoning and generalize to categories it has never encountered. This auxiliary information can include semantic descriptions of the categories, attributes, or other prior knowledge.
Zero-Shot Learning is a method that enables learning new categories without any samples. Typically, a model can only recognize the categories it has seen in the training set. However, through zero-shot learning, the model can leverage auxiliary information for reasoning and generalize to categories it has never encountered. This auxiliary information can include semantic descriptions of the categories, attributes, or other prior knowledge.