In the field of deep learning, large quantities of data are typically required to effectively train models. This challenge has given rise to techniques like zero-shot learning (ZSL), which trains models on a set of “seen” classes and evaluates them on a set of “unseen” classes. Although ZSL has shown considerable potential, particularly with the employment of generative methods, its generalizability to real-world scenarios remains uncertain.

中文翻译：

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基于属性的零样本学习的泛化性和鲁棒性评估

在深度学习领域，通常需要大量数据来有效地训练模型。这一挑战催生了零样本学习（ZSL）等技术，该技术在一组“已见”类别上训练模型，并在一组“未见”类别上对其进行评估。尽管 ZSL 显示出巨大的潜力，特别是在使用生成方法的情况下，但其对现实场景的普遍适用性仍然不确定。