论文标题
像素 - 全球自我监督的学习和不确定性感知的上下文稳定器
Pixel-global Self-supervised Learning with Uncertainty-aware Context Stabilizer
论文作者
论文摘要
我们开发了一种新颖的SSL方法，以捕获全球一致性和像素级的局部一致性，而相同图像的不同增强视图之间，以适应下游歧视性和密集的预测任务。我们采用了以前的对比SSL方法中使用的教师培训。在我们的方法中，通过汇总同一图像的增强视图的压缩表示来实现全局一致性。像素级的一致性是通过在不同的增强视图中追求相同像素的类似表示来实现的。重要的是，我们引入了一种不确定性感知的上下文稳定剂，以适应从不同的增强中产生的两个视图所产生的上下文差距。此外，我们在稳定器中使用了蒙特卡洛辍学，以不同视图中同一像素的表示之间的差异来测量不确定性并适应平衡差异。
We developed a novel SSL approach to capture global consistency and pixel-level local consistencies between differently augmented views of the same images to accommodate downstream discriminative and dense predictive tasks. We adopted the teacher-student architecture used in previous contrastive SSL methods. In our method, the global consistency is enforced by aggregating the compressed representations of augmented views of the same image. The pixel-level consistency is enforced by pursuing similar representations for the same pixel in differently augmented views. Importantly, we introduced an uncertainty-aware context stabilizer to adaptively preserve the context gap created by the two views from different augmentations. Moreover, we used Monte Carlo dropout in the stabilizer to measure uncertainty and adaptively balance the discrepancy between the representations of the same pixels in different views.
