  Large-scale datasets are the cornerstone of self-supervised representation
learning. Existing algorithms extract learning signals by making certain
assumptions about the data, e.g., spatio-temporal continuity and multimodal
correspondence. Unfortunately, finding a large amount of data that satisfies
such assumptions is sometimes not straightforward. This restricts the community
to rely on datasets that require laborious annotation and/or manual filtering
processes. In this paper, we describe a subset optimization approach for
automatic dataset curation. Focusing on the scenario of audio-visual
representation learning, we pose the problem as finding a subset that maximizes
the mutual information between audio and visual channels in videos. We
demonstrate that our approach finds videos with high audio-visual
correspondence and show that self-supervised models trained on our data,
despite being automatically constructed, achieve similar downstream
performances to existing video datasets with similar scales. The most
significant benefit of our approach is scalability. We release the largest
video dataset for audio-visual research collected automatically using our
approach.
