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FASL is the combination of Few-shot learning, which addresses the problem of learning new, unseen concepts quickly with a limited number of annotated training samples and Active learning, which is based on the idea that smart sampling of data leads to faster training and more accurate models.
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Few-shot learning
Few-shot learning refers to the model’s ability toclassify new data when only a limited number of training instances (e.g., 10 to 100) have been provided. As a result, after being exposed to a small amount of prior information, the model improves its performance.
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Text Classification is the process of categorizing text into one or more different classes to organize, structure, and filter into any parameter.
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Zero-shot learning
Zero-shot learning refers to the model’s ability to classify objects that it has never seen before, or in other words, allow us to assign an appropriate label to a piece of text without having received any training examples before.
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