インターネットエクスプローラー：オープンウェブ上でのターゲット表現学習
Internet Explorer: Targeted Representation Learning on the Open Web
静的な大規模データセットに頼らず、インターネットを自律的に探索して目的のタスクに有用な画像を収集し、小規模モデルを効率的に訓練する手法を提案。
著者: Alexander C. Li, Ellis Brown, Alexei A. Efros, Deepak Pathak
分類: cs.LG, cs.AI, cs.CV, cs.NE, cs.RO
原文アブストラクト
Modern vision models typically rely on fine-tuning general-purpose models pre-trained on large, static datasets. These general-purpose models only capture the knowledge within their pre-training datasets, which are tiny, out-of-date snapshots of the Internet -- where billions of images are uploaded each day.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. Results, visualizations, and videos at https://internet-explorer-ssl.github.io/
