Unveiling Zero-Shot & Few-Shot Learning: Pros, Cons & Insights

The article provides a comparative analysis of two machine learning methods: Zero-Shot Learning and Few-Shot Learning. Zero-Shot Learning, which leverages semantic relationships between classes, excels in situations with a lack of labeled data, but its performance depends on the quality of semantic representations. Few-Shot Learning, on the other hand, makes predictions based on a small amount of data by leveraging prior knowledge from related tasks, but its performance can be affected by the quality and relevance of the few examples.
PROMPT ENGINEERING SLIDES 4
PROMPT ENGINEERING SLIDES 4
        


Learning Method Description Advantages Disadvantages
Zero-Shot Learning
Zero-shot learning is a machine learning concept where a model is able to understand and make decisions on data it has never seen before. This is achieved by leveraging semantic relationships between classes. It's particularly useful when there's a lack of labeled data for certain classes.
  • Ability to handle classes not seen during training
  • Useful when there's a lack of labeled data
  • Can generalize learning from seen to unseen classes
  • Performance heavily depends on the quality of the semantic representations
  • May struggle with complex data and relationships
Few-Shot Learning
Few-shot learning is a technique where a machine learning model is able to make predictions based on a very small amount of data. This is achieved by leveraging prior knowledge and understanding from related tasks. It's particularly useful when there's limited data available.
  • Ability to learn from a small number of examples
  • Useful when there's limited data available
  • Can leverage knowledge from related tasks
  • Performance can be affected by the quality and relevance of the few examples
  • May struggle with tasks that are very different from the training tasks



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