用LeJEPA和超小型模型学习射电天文表示
Learning Radio Astronomical Representations with LeJEPA and Very Small Models
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中文总结 AI 辅助
该研究用约600万参数的超小型视觉模型结合LeJEPA，直接从射电天文观测自监督学习表示，在三项数据集上实现了与更大基础模型相当的射电星系分类性能，证明了表示学习目标选择对小型领域模型的重要性。
中文摘要 AI 辅助
已有研究表明，在自然图像上预训练的视觉基础模型学到的表示对域外天文图像有用。科学下游任务的性能随模型规模增大而提升，但即使考虑参数高效适配，这也会带来更高的推理成本并限制可扩展性。替代方案是通过自监督预训练直接从天文观测而非自然图像中学习表示。我们评估了LeJEPA的能力：使用约600万参数的超小型视觉模型在Radio Galaxy Zoo图像上预训练以学习鲁棒表示，并与成熟的自监督框架对比；我们测试LeJEPA的潜在空间正则化是否能提升射电星系形态分类性能。在三个评估数据集上，LeJEPA实现了与大得多的基础模型相当的性能，同时在训练和评估数据集间生成更一致的表示。这些结果表明，选择表示学习目标对让小型领域特定模型达到与从大型基础模型迁移的表示相当的科学成像性能至关重要。
英文摘要
Representations learned by vision foundation models pretrained on natural images have been shown to be useful for out-of-domain astronomical images. Performance on scientific downstream tasks increases with model size, which both carries higher inference costs and limits scalability, even when considering parameter-efficient adaptation. An alternative is to learn representations directly from astronomical observations rather than natural images, through self-supervised pretraining. We evaluate LeJEPA's ability to learn robust representations using very small vision models ($\sim$6M parameters) pretrained on Radio Galaxy Zoo images, comparing with established self-supervised frameworks. We test whether LeJEPA's latent-space regularization leads to better radio galaxy morphology classification. Across three evaluation datasets, LeJEPA achieves performance comparable to a substantially larger foundation model while producing more consistent representations across training and evaluation datasets. These results suggest that the choice of representation learning objective is critical for enabling small domain-specific models to achieve performance competitive with representations transferred from large foundation models in scientific imaging.
发表机构
- University of Geneva（日内瓦大学）
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