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Zero shot, one shot and few shot learning: Difference between revisions
Zero shot, one shot and few shot learning (view source)
Revision as of 13:20, 6 March 2023
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(Created page with "{{Needs Expansion}} Zero shot learning is when you have no examples in the prompt. One shot learning is when you have 1 example in the prompt. Few shot learning is when you have a few examples in the prompt. all of these techniques allow the machine learning model to learn with limited or no labeled data. ==Zero Shot Learning== Zero-shot learning, one-shot learning, and few-shot learning are all machine learning techniques used to train model...") |
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==One Shot Learning== | ==One Shot Learning== | ||
One-shot learning and few-shot learning are sub-fields of machine learning and computer vision that focus on recognizing objects or classifying data with very limited data. | |||
One-shot learning refers to a scenario where the machine learning model is trained on only one example of each class. The goal is to learn from this single example and generalize to new, unseen examples of the same class. In other words, the model must be able to recognize the class based on a single example and classify new, similar examples accurately. | |||
==Few Shot Learning== | ==Few Shot Learning== | ||
Few-shot learning is similar to one-shot learning, but it refers to a scenario where the model is trained on a small number of examples, typically between 2 and 20 examples per class. The goal is still to learn from these few examples and generalize to new, unseen examples of the same class. | |||
===Example=== | ===Example=== | ||