遥感中基于掩码自编码器的持续自监督学习
Continual Self-Supervised Learning with Masked Autoencoders in Remote Sensing
AI总结：
针对遥感中持续学习依赖大量标注数据的问题，提出持续自监督学习方法CoSMAE，通过数据mixup与模型mixup知识蒸馏在数据和模型层面正则化掩码自编码器，有效缓解灾难性遗忘，性能提升达4.94%。
摘要：
持续学习（CL）方法旨在从持续获取的训练数据中以顺序方式学习新任务，其发展在遥感（RS）领域引起了广泛关注。现有的遥感CL方法在学习新任务的同时，增强了对灾难性遗忘的鲁棒性。这是通过使用大量标注训练样本实现的，而在遥感中这成本高昂且并不总是可行。为解决此问题，我们提出了一种在掩码自编码器背景下的新型持续自监督学习方法（记为CoSMAE）。所提出的CoSMAE包含两个组件：i）数据mixup；ii）模型mixup知识蒸馏。数据mixup通过将当前任务的图像与先前任务的图像进行插值，来保留先前数据分布的信息。模型mixup知识蒸馏通过对过去模型和当前模型的权重进行插值以形成知识蒸馏的教师模型，从而同时从两者中蒸馏知识。这两个组件相互补充，在数据和模型层面上对MAE进行正则化，以促进跨任务的更好泛化并降低灾难性遗忘的风险。实验结果表明，与应用于MAE的最先进CL方法相比，CoSMAE实现了高达4.94%的显著提升。我们的代码公开于：https://git.tu-berlin.de/rsim/CoSMAE。
英文摘要：
The development of continual learning (CL) methods, which aim to learn new tasks in a sequential manner from the training data acquired continuously, has gained great attention in remote sensing (RS). The existing CL methods in RS, while learning new tasks, enhance robustness towards catastrophic forgetting. This is achieved by using a large number of labeled training samples, which is costly and not always feasible to gather in RS. To address this problem, we propose a novel continual self-supervised learning method in the context of masked autoencoders (denoted as CoSMAE). The proposed CoSMAE consists of two components: i) data mixup; and ii) model mixup knowledge distillation. Data mixup is associated with retaining information on previous data distributions by interpolating images from the current task with those from the previous tasks. Model mixup knowledge distillation is associated with distilling knowledge from past models and the current model simultaneously by interpolating their model weights to form a teacher for the knowledge distillation. The two components complement each other to regularize the MAE at the data and model levels to facilitate better generalization across tasks and reduce the risk of catastrophic forgetting. Experimental results show that CoSMAE achieves significant improvements of up to 4.94% over state-of-the-art CL methods applied to MAE. Our code is publicly available at: https://git.tu-berlin.de/rsim/CoSMAE.
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