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Modern vision models typically rely on fine-tuning general-purpose modelspre-trained on large, static datasets. These general-purpose models onlycapture the knowledge within their pre-training datasets, which are tiny,out-of-date snapshots of the Internet -- where billions of images are uploadedeach day. We suggest an alternate approach: rather than hoping our staticdatasets transfer to our desired tasks after large-scale pre-training, wepropose dynamically utilizing the Internet to quickly train a small-scale modelthat does extremely well on the task at hand. Our approach, called InternetExplorer, explores the web in a self-supervised manner to progressively findrelevant examples that improve performance on a desired target dataset. Itcycles between searching for images on the Internet with text queries,self-supervised training on downloaded images, determining which images wereuseful, and prioritizing what to search for next. We evaluate Internet Exploreracross several datasets and show that it outperforms or matches CLIP oracleperformance by using just a single GPU desktop to actively query the Internetfor 30--40 hours. Results, visualizations, and videos athttps://internet-explorer-ssl.github.io/

Internet Explorer: Targeted Representation Learning on the Open Web