CITATION.cff

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title: Internet Explorer
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Ellis
    name-particle: L
    family-names: Brown
    name-suffix: II
    email: ellisbrown@cmu.edu
    affiliation: Carnegie Mellon University
    orcid: 'https://orcid.org/0000-0002-8117-0778'
  - given-names: Alexander
    name-particle: C
    family-names: Li
    email: alexanderli@cmu.edu
    affiliation: Carnegie Mellon University
    orcid: 'https://orcid.org/0000-0002-9884-6383'
repository-code: 'https://github.com/internet-explorer-ssl/internet-explorer'
url: 'https://internet-explorer-ssl.github.io'
abstract: >-
  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  their pre-training datasets, which are tiny, out-of-date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.
keywords:
  - Machine Learning
  - Computer Vision
  - Self-Supervised Learning
  - Representation Learning
  - Online Learning
license: MIT