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Few-shot learning and one-shot learning may refer to: Few-shot learning, a form of prompt engineering in generative AI; One-shot learning (computer vision)
GPT-3 is capable of performing zero-shot and few-shot learning (including one-shot). [ 1 ] In June 2022, Almira Osmanovic Thunström wrote that GPT-3 was the primary author on an article on itself, that they had submitted it for publication, [ 24 ] and that it had been pre-published while waiting for completion of its review.
Few-shot learning A prompt may include a few examples for a model to learn from, such as asking the model to complete " maison → house, chat → cat, chien →" (the expected response being dog ), [ 31 ] an approach called few-shot learning .
One-shot learning is an object categorization problem, found mostly in computer vision. Whereas most machine learning -based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one, or only a few, examples.
CLIP has been used as a component in multimodal learning. For example, during the training of Google DeepMind's Flamingo (2022), [33] the authors trained a CLIP pair, with BERT as the text encoder and NormalizerFree ResNet F6 [34] as the image encoder. The image encoder of the CLIP pair was taken with parameters frozen and the text encoder was ...
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The name is a play on words based on the earlier concept of one-shot learning, in which classification can be learned from only one, or a few, examples. Zero-shot methods generally work by associating observed and non-observed classes through some form of auxiliary information, which encodes observable distinguishing properties of objects. [1]