Vision models heavily rely on fine-tuning general-purpose models pre-trained on large, static datasets. These general-purpose models only understand knowledge within their pre-training datasets, which are tiny, out-of-date snapshots of the Internet—where billions of images are uploaded each day.

We suggest an alternate approach: rather than hoping our static datasets transfer to our desired tasks after large-scale pre-training, we propose dynamically utilizing the Internet to quickly train a small-scale model that does extremely well on the task at hand. Our approach, called Internet Explorer, explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images were useful, and prioritizing what to search for next.

We evaluate Internet Explorer across several datasets and show that it outperforms or matches CLIP oracle performance by using just a single GPU desktop to actively query the Internet for 30–40 hours.

Given unlabeled data for a target task, our approach, Internet Explorer, searches the Internet to progressively find more and more relevant training data via self-supervised training.

Internet Explorer iteratively repeats 4 steps to find and learn from relevant Internet data. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images are relevant to the target dataset, and prioritizing what to search for next.

Coming soon!

If you find this work useful in your research, please cite:

@misc{li2023internet,
    title={Internet Explorer: Targeted Representation Learning on the Open Web}, 
    author={Alexander C. Li and Ellis Brown and Alexei A. Efros and Deepak Pathak},
    year={2023},
    eprint={2302.14051},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}