论文标题
扩展和分析跨领域的自我监督学习
Extending and Analyzing Self-Supervised Learning Across Domains
论文作者
论文摘要
近年来，自我监督的表示学习取得了令人印象深刻的成绩，实验主要出现在ImageNet或其他类似大型的Internet图像数据集上。这些方法在其他较小的领域（例如卫星，纹理或生物图像）上几乎没有工作。我们在空前的各种域上尝试了几种流行方法。我们发现，除其他发现外，旋转是迄今为止最有意义的任务，拼图和实例歧视的大部分表现归因于其诱导分布的性质而不是语义理解。此外，还有几个领域，例如细粒度分类，所有任务表现不佳。我们通过研究借口概括，随机标记和隐性维度的新实验进行定量和定性地诊断这些故障和成功的原因。代码和型号可在https://github.com/bramsw/extgending_ssrl_across_domains/上找到。
Self-supervised representation learning has achieved impressive results in recent years, with experiments primarily coming on ImageNet or other similarly large internet imagery datasets. There has been little to no work with these methods on other smaller domains, such as satellite, textural, or biological imagery. We experiment with several popular methods on an unprecedented variety of domains. We discover, among other findings, that Rotation is by far the most semantically meaningful task, with much of the performance of Jigsaw and Instance Discrimination being attributable to the nature of their induced distribution rather than semantic understanding. Additionally, there are several areas, such as fine-grain classification, where all tasks underperform. We quantitatively and qualitatively diagnose the reasons for these failures and successes via novel experiments studying pretext generalization, random labelings, and implicit dimensionality. Code and models are available at https://github.com/BramSW/Extending_SSRL_Across_Domains/.
