Abstract: Corruptions due to data perturbations and label noise are prevalent in the datasets from unreliable sources, which poses significant threats to model training. Despite existing efforts in ...
Our approach models label noise in semantic segmentation with a probabilistic framework that incorporates spatial correlations. By introducing a continuous latent ...
Abstract: Multi-label stream classification aims to address the challenge of dynamically assigning multiple labels to sequentially-arrived instances. In real situations, only partial labels of ...
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