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, are promising for producing views in this setting without requiring extensive domain knowledge and trial and error. We apply Viewmaker to four multispectral imaging problems, each with a different format, finding that Viewmaker 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 https://github.com/jbayrooti/divmaker.

翻译：对比学习方法通过训练模型识别数据点的相似“视图”，已应用于多个领域和模态。然而，专业科学模态给这一范式带来挑战：为每个科学仪器确定合适的视图既复杂又耗时。本文聚焦于将对比学习方法应用于多种遥感数据集。我们证明，近期提出的视图生成网络（Viewmaker networks）可在无需大量领域知识和试错的情况下生成有效视图。我们将视图生成网络应用于四个不同格式的多光谱成像问题，发现其在下游分类任务评估中均优于基于裁剪和反射的对比学习方法。这为领域无关方法能够推动对比学习扩展到真实世界科学领域提供了进一步证据。开源代码见https://github.com/jbayrooti/divmaker。