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Zero-shot studying (ZSL) represents a innovative technique to deep understanding acquisition, permitting models to become aware of and classify studying that has not been encountered across faculties. By the usage of semantic illustration and embedding techniques, ZSL addresses the limitations of conventional supervised gaining knowledge of, which is based closely on categorized information This paper explores ZSL’s theoretical foundations, techniques and applications. The paper identifies states of concern on crucial functions related to ZSL, together with troubles of switch and semantic holes, and explores contemporary trends such as gaining knowledge of and numerous foundational models. The findings spotlight the potential of ZSL to redefine the device to gain information of scalability while simultaneously offering the roadmap for enrichment improvement.

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Zero-Shot Learning: Enabling Deep Learning Models to Recognize Unseen Categories

  • 会议论文

  • 发表日期：

    2026年

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  • Madhav Sharma,
  • Pushpendra Sikarwal,
  • Samiksha Agarwal

摘要

Zero-shot studying (ZSL) represents a innovative technique to deep understanding acquisition, permitting models to become aware of and classify studying that has not been encountered across faculties. By the usage of semantic illustration and embedding techniques, ZSL addresses the limitations of conventional supervised gaining knowledge of, which is based closely on categorized information This paper explores ZSL’s theoretical foundations, techniques and applications. The paper identifies states of concern on crucial functions related to ZSL, together with troubles of switch and semantic holes, and explores contemporary trends such as gaining knowledge of and numerous foundational models. The findings spotlight the potential of ZSL to redefine the device to gain information of scalability while simultaneously offering the roadmap for enrichment improvement.