Domain adaptation (DA) strives to mitigate the domain gap between the source domain where a model is trained, and the target domain where the model is deployed. When a deep learning model is deployed on an aerial platform, it may face gradually degrading weather conditions during operation, leading to widening domain gaps between the training data and the encountered evaluation data. We synthesize two such gradually worsening weather conditions on real images from two existing aerial imagery datasets, generating a total of four benchmark datasets. Under the continual, or test-time adaptation setting, we evaluate three DA models on our datasets: a baseline standard DA model and two continual DA models. In such setting, the models can access only one small portion, or one batch of the target data at a time, and adaptation takes place continually, and over only one epoch of the data. The combination of the constraints of continual adaptation, and gradually deteriorating weather conditions provide the practical DA scenario for aerial deployment. Among the evaluated models, we consider both convolutional and transformer architectures for comparison. We discover stability issues during adaptation for existing buffer-fed continual DA methods, and offer gradient normalization as a simple solution to curb training instability.

翻译：域自适应旨在缓解模型训练的源域与部署的目标域之间的域差异。当深度学习模型部署于空中平台时，其在运行过程中可能面临逐渐恶化的天气条件，导致训练数据与实测评估数据之间的域差异持续扩大。我们基于两个现有航拍图像数据集中的真实图像，合成了两种渐进恶化的天气条件，共生成四个基准数据集。在持续或测试时自适应设定下，我们评估了三种域自适应模型：一个基线标准域自适应模型与两个持续域自适应模型。在此设定中，模型每次只能访问目标数据的一小部分（即一个批次），自适应过程持续进行，且仅遍历数据一次。持续自适应的约束条件与渐进恶化的天气条件相结合，为空中部署提供了实际的域自适应场景。在评估的模型中，我们分别采用卷积架构与Transformer架构进行对比分析。我们发现现有缓存驱动的持续域自适应方法在自适应过程中存在稳定性问题，并提出梯度归一化作为抑制训练不稳定的简单解决方案。