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Related Questions
- What are the key differences between self-supervised and supervised learning in the context of analogy recognition and generation?
- How can contrastive learning be adapted to handle analogies with multiple steps or hierarchical relationships?
- What are the challenges of scaling up contrastive learning for large-scale analogy datasets, and how can they be addressed?
- How does contrastive learning compare to other techniques such as metric learning or knowledge graph-based methods in analogy recognition and generation?
- What are some common evaluation metrics used to assess the performance of contrastive learning-based analogy models, and how can they be improved?
- How can contrastive learning be used in conjunction with other techniques such as transfer learning or multi-task learning to improve analogy recognition and generation?
- What are the potential applications of contrastive learning in analogy recognition and generation, such as in natural language processing or computer vision?
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