论文标题
弱监督课程细分的持续学习
Weakly-supervised continual learning for class-incremental segmentation
论文作者
论文摘要
转移学习是一种在遥感中将现有深度学习模型调整到新的新兴用例中的有力方法。从已经训练着语义细分的神经网络开始，我们建议修改其标签空间，以迅速将其适应在弱监督下的新课程中。为了减轻背景转变和这种持续学习形式固有的灾难性遗忘问题，我们比较了不同的正则化术语并利用了伪标签策略。我们通过实验表明我们的方法在三个公共遥感数据集上的相关性。代码是开放式的，并在此存储库中发布：https：//github.com/alteia-ai/icss} {https://github.com/alteia-ai/icss。
Transfer learning is a powerful way to adapt existing deep learning models to new emerging use-cases in remote sensing. Starting from a neural network already trained for semantic segmentation, we propose to modify its label space to swiftly adapt it to new classes under weak supervision. To alleviate the background shift and the catastrophic forgetting problems inherent to this form of continual learning, we compare different regularization terms and leverage a pseudo-label strategy. We experimentally show the relevance of our approach on three public remote sensing datasets. Code is open-source and released in this repository: https://github.com/alteia-ai/ICSS}{https://github.com/alteia-ai/ICSS.
