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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)
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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. | 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. | ||
===Example=== | |||
For example, imagine you want to build a system that can recognize different handwritten digits. In a typical machine learning setup, you would need to provide the model with a large number of examples of each digit, and the model would learn to recognize each digit by finding patterns in the training data. In one shot learning, however, you might only provide the model with a single example of each digit, and the model would have to learn to recognize new instances of each digit based on that single example. | |||
==Few Shot Learning== | ==Few Shot Learning== | ||