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
this https URL.

本篇论文探讨了如何运用对比学习的方法，使用 Viewmaker 网络产生视角从而处理多种遥感数据集，同时发现该方法在每个多光谱成像问题上表现优于基于裁剪和反射的方法，结果表明领域无关的方法可以扩展应用到实际的科学领域。

使用 Viewmaker 网络进行多光谱自监督学习

Multispectral Self-Supervised Learning with Viewmaker Networks

Many recent methods for unsupervised representation learning train models to
be invariant to different "views," or distorted versions of an input. However,
designing these views requires considerable trial and error by human experts,
hindering widespread adoption of unsupervised representation learning methods
across domains and modalities. To address this, we propose viewmaker networks:
generative models that learn to produce useful views from a given input.
Viewmakers are stochastic bounded adversaries: they produce views by generating
and then adding an $\ell_p$-bounded perturbation to the input, and are trained
adversarially with respect to the main encoder network. Remarkably, when
pretraining on CIFAR-10, our learned views enable comparable transfer accuracy
to the well-tuned SimCLR augmentations -- despite not including transformations
like cropping or color jitter. Furthermore, our learned views significantly
outperform baseline augmentations on speech recordings (+9% points, on average)
and wearable sensor data (+17% points). Viewmakers can also be combined with
handcrafted views: they improve robustness to common image corruptions and can
increase transfer performance in cases where handcrafted views are less
explored. These results suggest that viewmakers may provide a path towards more
general representation learning algorithms -- reducing the domain expertise and
effort needed to pretrain on a much wider set of domains. Code is available at
this https URL

本文提出了一种称为 viewmaker networks 的生成模型，它可以从输入中生成有用的视图，并在预先训练时实现了与手工制作视图相当的结果，从而降低了预训练需要的领域专业知识和努力。