In recent years, great progress has been made in the field of cross-scene classification. However, most existing cross-scene methods assume that the source domain has massive and carefully annotated data, which is time-consuming and labor-intensive in practice. Datasets in real-life applications usually contain a large number of noisy labels, which will significantly affect cross-scene classification performance. How to perform more discriminative and generalized cross-scene classification in the presence of noisy samples needs to be urgently addressed. Apart from that, existing methods tend to implement global matching between domains, causing problems such as unbalanced adaptation and negative transfer, limiting the cross-scene performance of the model. For more effective and reliable cross-scene classification under noisy environment, robust adaptation with noise (RAN) is proposed in this article. RAN explores which samples are noiseless and transferable to enable positive and robust cross-scene transfer. The curriculum learning strategy is used to filter out noisy samples for better source supervised learning and cross-domain matching. To further improve the stability and effectiveness of cross-scene adaptation, the class weighting factor and the public weighting factor are introduced to consider the class information of the source and target domains. RAN is an efficient plug-and-play adaptation framework, which is easily implemented and can be embedded in existing methods. Experimental results demonstrate that the proposed RAN can achieve remarkable performance on cross-scene classification tasks in noisy environments.

Cross-scene classification has recently gained increasing interest, which improves the classification performance on label-scarce domains by transferring knowledge learned from label-rich domains. Domain adaptation (DA) attempts to solve the domain gap problem and it is widely used in the cross-scene applications. The priori knowledge that the label space between the source and target domains is identical is a key requirement for existing cross-scene approaches to work successfully. However, label sets of different domains can always be different in real applications, that is to say, there will be common as well as private categories for different domains. Universal DA (UniDA) has been proposed to deal with the above difficulty by relaxing all constraints on the label sets. In order to complete more general remote sensing cross-scene classification tasks regardless of label sets, we propose a UniDA cross-scene classification approach, adversarial divergence training (ADT), to simultaneously classify the target common categories and detect the target private categories based on the divergence of different classifiers. ADT attempts to train the classifier and feature extractor against each other (in adversarial) in order to extract more domain-invariant and discriminative features. At the same time, divergence optimization of different classifiers is used to distinguish the target private class. The former makes it capable of cross-scene tasks, while the latter weakens the effect of the label set on the performance of the algorithm. Experiments show that ADT outperforms baselines in the UniDA setting and even in other settings.

In recent years, classification has obtained ever-rising attention and has been applied to many areas in the field of remote sensing, including land use, forest monitoring, urban planning, and vegetation management. Due to the lack of labeled data and the poor generalization ability of supervised models, cross-scene classification is proposed for better utilization of the existing knowledge. Existing adaptation methods for cross-scene classification only consider the marginal distribution, while the conditional distribution is equally important in real applications. In addition, approaches based on deep learning align the distribution of features extracted from a single-scale structure, leading to the loss of information. To overcome the above drawbacks, an Attention-based Multiscale Residual Adaptation Network (AMRAN) is proposed for cross-scene classification tasks. In the proposed AMRAN, both the marginal and conditional distributions are taken into consideration for more comprehensive alignment. Besides, the attention mechanism and the multiscale strategy are used to extract more robust features and more complete information, respectively. Experimental results between four existing scene classification data sets demonstrate that AMRAN has a significant improvement compared with the state-of-the-art deep adaptation methods.

Domain adaptation is widely used in the field of remote sensing, which can transfer the existing knowledge to new tasks and promote performance. When applied in the field of scene classification, it can be called cross-scene classification. Previous cross-scene classification methods mainly consider the coarse-grained alignment in the global aspect, which may ignore the structures behind the data and lose the local information with respect to specific categories. To implement fine-grained alignment, we present an adversarial fine-grained adaptation network (AFGAN) which simultaneously captures the complex structures behind the data distributions to improve the discriminability and reduce the local discrepancy of different domains to align the relevant category distributions. Experimental results based on three existing scene classification datasets demonstrate the effectiveness of AFGAN.

Sepsis treatment is a highly challenging effort to reduce mortality in hospital intensive care units since the treatment response may vary for each patient. Tailored treatment recommendations are desired to assist doctors in making decisions efficiently and accurately. In this work, we apply a self-supervised method based on reinforcement learning (RL) for treatment recommendation on individuals. An uncertainty evaluation method is proposed to separate patient samples into two domains according to their responses to treatments and the state value of the chosen policy. Examples of two domains are then reconstructed with an auxiliary transfer learning task. A distillation method of privilege learning is tied to a variational auto-encoder framework for the transfer learning task between the low- and high-quality domains. Combined with the self-supervised way for better state and action representations, we propose a deep RL method called high-risk uncertainty (HRU) control to provide flexibility on the trade-off between the effectiveness and accuracy of ambiguous samples and to reduce the expected mortality. Experiments on the large-scale publicly available real-world dataset MIMIC-III demonstrate that our model reduces the estimated mortality rate by up to 2.3% in total, and that the estimated mortality rate in the majority of cases is reduced to 9.5%.