Self-supervised representation learning methods often fail to learn subtle or complex features, which can be dominated by simpler patterns which are much easier to learn. This limitation is particularly problematic in applications to science and engineering, as complex features can be critical for discovery and analysis. To address this, we introduce Split Component Embedding Registration (SpliCER), a novel architecture which splits the image into sections and distils information from each section to guide the model to learn more subtle and complex features without compromising on simpler features. SpliCER is compatible with any self-supervised loss function and can be integrated into existing methods without modification. The primary contributions of this work are as follows: i) we demonstrate that existing self-supervised methods can learn shortcut solutions when simple and complex features are both present; ii) we introduce a novel self-supervised training method, SpliCER, to overcome the limitations of existing methods, and achieve significant downstream performance improvements; iii) we demonstrate the effectiveness of SpliCER in cutting-edge medical and geospatial imaging settings. SpliCER offers a powerful new tool for representation learning, enabling models to uncover complex features which could be overlooked by other methods.

本研究解决了自监督表示学习方法在学习细微或复杂特征方面的不足，尤其是在科学和工程应用中的局限性。我们提出了一种新颖的架构SpliCER，通过将图像分割成不同部分而提取信息，帮助模型学习复杂特征，同时保留简单特征，从而显著提升下游应用的表现。SpliCER在医疗和地理空间成像中的有效性展示了其作为表示学习的新工具的潜力。

分而治之的自监督学习用于高内容成像