Few-shot learning is a concept in machine learning where a model is designed to accurately predict or categorize new data after seeing only a few examples of each class during the training process.
Consider a situation where you move to a new city and only meet a few individuals from that city. Based on your interaction with these individuals, you start making general observations about the people of the city. This is akin to how few-shot learning works.
Here’s a more detailed explanation: In traditional machine learning, models typically learn from large amounts of data, with many examples from each class. However, in many real-world scenarios, we often don’t have the luxury of large labeled datasets. Few-shot learning addresses this problem by developing models that can understand new classes from only a few examples.
There are various techniques to accomplish this. One common approach is meta-learning (or “learning to learn”). In this approach, the model is trained on a variety of tasks, each with a small number of examples, and learns an algorithm to quickly learn a new task from a few examples. Then, when given a new task with a few examples, the model applies the learned algorithm to quickly adapt to the new task.
Few-shot learning is particularly useful in domains where data is scarce or expensive to obtain. However, it is a challenging task, as the model must learn to generalize well from very few examples.
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