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Found 6 papers, 1 papers with code
DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images
We propose a novel approach, coined DAugNet, for unsupervised, multi-source, multi-target, and life-long domain adaptation of satellite images.
StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization
However, these methods have limited practical real world applications, since usually one has multiple source domains with different data distributions.
SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data
Although convolutional neural networks have been proven to be an effective tool to generate high quality maps from remote sensing images, their performance significantly deteriorates when there exists a large domain shift between training and test data.
ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks
Due to the various reasons such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between spectral bands of satellite images collected from different geographic locations.
Incremental Learning for Semantic Segmentation of Large-Scale Remote Sensing Data
The key points of the proposed approach are adapting the network to learn new as well as old classes on the new training data, and allowing it to remember the previously learned information for the old classes.
Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis
Multi-instance multi-label (MIML) learning is a challenging problem in many aspects.
