Learning Transferable Visual Models From Natural …
Learning Transferable Visual Models From Natural Language SupervisionAlec Radford* 1Jong Wook Kim* 1Chris Hallacy1Aditya Ramesh1Gabriel Goh1Sandhini Agarwal1Girish Sastry1Amanda Askell1Pamela Mishkin1Jack Clark1Gretchen Krueger1Ilya Sutskever1AbstractState-of-the-art computer vision systems aretrained to predict a fixed set of predeterminedobject categories. This restricted form of super-vision limits their generality and usability sinceadditional labeled data is needed to specify anyother Visual concept. Learning directly from rawtext about images is a promising alternative whichleverages a much broader source of demonstrate that the simple pre-training taskof predicting which caption goes with which im-age is an efficient and scalable way to learn SOTAimage representations from scratch on a dataseto
other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which im-age is an efficient and scalable way to learn SOTA image representations from scratch on a dataset
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