Few-shot learning is a concept in machine learning where the designed model is capable of making predictions based on learning from a very small amount of data - as few as 1-5 data points. This approach aims to create systems that can understand new concepts, phenomena or objects with minimal examples presented, imitating human cognitive ability to learn quickly from a few examples.
How Few-Shot Learning works
The process of few-shot learning involves two key stages: training and testing. During the training phase, a model is exposed to a large variety of tasks with plenty of examples for each. The model, which is typically a neural network, learns to extract general knowledge from these tasks and their related examples which can aid in understanding new tasks.
In the second stage, the model is given a new task with only a few examples (hence 'few-shot'). The model must then use the general principles it has learned from the training phase to understand and perform this new task effectively, despite having only a few examples to learn from.
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