Transcription of Learning Transferable Visual Models From Natural Language ...
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Learning Transferable Visual Models From Natural Language ...
Learning Transferable Visual Models From Natural Language SupervisionAlec Radford* 1 Jong Wook Kim* 1 Chris Hallacy1 Aditya Ramesh1 Gabriel Goh1 Sandhini Agarwal1 Girish Sastry1 Amanda Askell1 Pamela Mishkin1 Jack Clark1 Gretchen Krueger1 Ilya Sutskever1 AbstractState-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 SOTA image representations from scratch on a datasetof 400 million (image, text) pairs collected fromthe internet.
vised, self-supervised, weakly supervised, and supervised respectively. We emphasize that what is common across this line of work is not any of the details of the particular methods used but the appreciation of natural language as a training signal. All these approaches are learning from natural language super-vision.
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