Self-supervised learning excels in learning representations from large amounts of unlabeled data, demonstrating success across multiple data modalities. Yet, extending self-supervised learning to new modalities is non-trivial because the specifics of existing methods are tailored to each domain, such as domain-specific augmentations which reflect the invariances in the target task. While masked modeling is promising as a domain-agnostic framework for self-supervised learning because it does not rely on input augmentations, its mask sampling procedure remains domain-specific. We present Self-guided Masked Autoencoders (SMA), a fully domain-agnostic masked modeling method. SMA trains an attention based model using a masked modeling objective, by learning masks to sample without any domain-specific assumptions. We evaluate SMA on three self-supervised learning benchmarks in protein biology, chemical property prediction, and particle physics. We find SMA is capable of learning representations without domain-specific knowledge and achieves state-of-the-art performance on these three benchmarks.

翻译：自监督学习在从大量无标注数据中学习表示方面表现出色，已在多种数据模态中取得成功。然而，将自监督学习扩展到新模态并非易事，因为现有方法的具体细节针对每个领域定制，例如反映目标任务不变性的领域特定数据增强。尽管掩码建模作为自监督学习的领域无关框架前景广阔（因其不依赖输入增强），但其掩码采样过程仍具有领域特异性。我们提出自引导掩码自编码器（SMA），这是一种完全领域无关的掩码建模方法。SMA通过掩码建模目标训练基于注意力的模型，在学习掩码时无需任何领域特定假设。我们在蛋白质生物学、化学性质预测和粒子物理学三个自监督学习基准上评估SMA。实验发现，SMA能够在无领域特定知识的情况下学习表示，并在上述三个基准上达到最先进的性能。