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Found 4 papers, 2 papers with code
Learning Variational Neighbor Labels for Test-Time Domain Generalization
We formulate the generalization at test time as a variational inference problem by modeling pseudo labels as distributions to consider the uncertainty during generalization and alleviate the misleading signal of inaccurate pseudo labels.
SKDCGN: Source-free Knowledge Distillation of Counterfactual Generative Networks using cGANs
Moreover, as an additional contribution, our paper conducts a thorough study on the composition mechanism of the CGNs, to gain a better understanding of how each mechanism influences the classification accuracy of an invariant classifier.
Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images
We show that popular choices of dataset selection suffer from data homogeneity, leading to misleading results.
Unsupervised Domain Adaptation for Semantic Segmentation of NIR Images through Generative Latent Search
Therefore we cast the skin segmentation problem as that of target-independent Unsupervised Domain Adaptation (UDA) where we use the data from the Red-channel of the visible-range to develop skin segmentation algorithm on NIR images.
