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. After pre-training, Natural languageis used to reference learned Visual concepts (ordescribe new ones) enabling zero-shot transferof the model to downstream tasks.
We measure this by benchmarking the zero-shot transfer performance of CLIP on over 30 existing datasets and find. Learning Transferable Visual Models From Natural Language Supervision 3 2M 33M 67M 134M 268M 400M # of images processed 0 5 10 15 20 25 30 35 40 Zero-Shot ImageNet Accuracy 4X efficiency 3X efficiency
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