Wearable devices enable constant monitoring of individual workers and the environment, whereas connected worker solutions provide contextual information and decision support.
Although various weather degradation synthesis methods exist in the literature, the use of synthetically generated weather degraded images often results in sub-optimal performance on the real weather degraded images due to the domain gap between synthetic and real-world images.
Magnetic Resonance (MR) image reconstruction from under-sampled acquisition promises faster scanning time.
Pseudo-label based self training approaches are a popular method for source-free unsupervised domain adaptation.
First, we learn generative features using the one-class data with a generative framework.
In particular, we introduce a temporal ensembling component to the objective function of DSC algorithms to enable the DSC networks to maintain consistent subspaces for random transformations in the input data.
Meta-learning aims to deliver an adaptive model that is sensitive to these underlying distribution changes, but requires many tasks during the meta-training process.