Abstract
Semi-supervised domain adaptation methods leverage information from a source labelled domain with the goal of generalizing over a scarcely labelled target domain. While this setting already poses challenges due to potential distribution shifts between domains, an even more complex scenario arises when source and target data differs in modality representation (e.g. they are acquired by sensors with different characteristics). For instance, in remote sensing, images may be collected via various acquisition modes (e.g. optical or radar), different spectral characteristics (e.g. RGB or multi-spectral) and spatial resolutions. Such a setting is denoted as Semi-Supervised Heterogeneous Domain Adaptation (SSHDA) and it exhibits an even more severe distribution shift due to modality heterogeneity across this http URL cope with the challenging SSHDA setting, here we introduce SHeDD (Semi-supervised Heterogeneous Domain Adaptation via Disentanglement) an end-to-end neural framework tailored to learning a target domain classifier by leveraging both labelled and unlabelled data from heterogeneous data sources. SHeDD is designed to effectively disentangle domain-invariant representations, relevant for the downstream task, from domain-specific information, that can hinder the cross-modality transfer. Additionally, SHeDD adopts an augmentation-based consistency regularization mechanism that takes advantages of reliable pseudo-labels on the unlabelled target samples to further boost its generalization ability on the target domain. Empirical evaluations on two remote sensing benchmarks, encompassing heterogeneous data in terms of acquisition modes and spectral/spatial resolutions, demonstrate the quality of SHeDD compared to both baseline and state-of-the-art competing approaches. Our code is publicly available here: this https URL
Abstract (translated)
半监督域适应方法利用带有标签的source域中的信息，以实现对目标域的泛化。然而，由于领域之间可能存在的分布变化，这种设置本身就具有挑战性。当源和目标数据在模态表示上不同（例如，它们是由具有不同特性的传感器获取的）时，情况变得更加复杂。例如，在遥感领域，图像可以通过各种采集模式（例如光学或雷达）以及不同的光谱特性和空间分辨率进行收集。这种设置被称为半监督异质领域自适应（SSHDA），并且由于模态异质性在这些http URL上表现更加严重，它对半监督SSHDA设置的挑战更大。在这里，我们引入了SHeDD（通过解耦来半监督异质领域自适应）这个端到端的神经框架，它专门为通过利用带有标签和未标记的数据源异质数据进行目标领域分类而设计。SHeDD旨在有效地解耦领域无关表示，这对于下游任务是相关的，同时利用无标记目标样本的可靠伪标签进一步增强其在目标领域的泛化能力。在两个遥感基准上的实证评估，包括 acquisition modes 和 spectral/spatial resolutions 中的异质数据，证明了SHeDD相对于基线和最先进的竞争方法具有更高的质量。我们的代码在这里是公开可用的：这个https URL。
URL
https://arxiv.org/abs/2406.14087
