Multispectral Contrastive Learning With Viewmaker Networks

Jasmine Bayrooti, Noah Goodman, Alex Tamkin; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 440-448


Contrastive learning methods have been applied to a range of domains and modalities by training models to identify similar "views" of data points. However, specialized scientific modalities pose a challenge for this paradigm, as identifying good views for each scientific instrument is complex and time-intensive. In this paper, we focus on applying contrastive learning approaches to a variety of remote sensing datasets. We show that Viewmaker networks, a recently proposed method for generating views without extensive domain knowledge, can produce useful views in this setting. We also present a Viewmaker variant called Divmaker, which achieves similar performance and does not require adversarial optimization. Applying both methods to four multispectral imaging problems, each with a different format, we find that Viewmaker and Divmaker can outperform cropping- and reflection-based methods for contrastive learning in every case when evaluated on downstream classification tasks. This provides additional evidence that domain-agnostic methods can empower contrastive learning to scale to real-world scientific domains. Open source code can be found at

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[pdf] [arXiv]
@InProceedings{Bayrooti_2023_CVPR, author = {Bayrooti, Jasmine and Goodman, Noah and Tamkin, Alex}, title = {Multispectral Contrastive Learning With Viewmaker Networks}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {440-448} }