Zero-shot learning (ZSL) enables machine learning models to categorize unseen concepts without needing specific training examples, differing from traditional methods that rely on extensive labeled datasets. This guide discusses ZSL's workings, comparisons with few-shot learning (FSL) and one-shot learning (OSL), its applications, benefits, and challenges. ZSL utilizes pre-training on large datasets to infer relationships between known and unknown classes, making it useful in fields like computer vision and natural language processing. However, it faces challenges like reliance on high-quality data and difficulties adapting to significantly different tasks.
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