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Real-world applications of machine learning models often confront data distribution shifts, wherein discrepancies exist between the training and test data distributions. In the common multi-domain multi-class setup, as the number of classes and domai
Externí odkaz:
http://arxiv.org/abs/2402.02851
Domain generalization asks for models trained over a set of training environments to generalize well in unseen test environments. Recently, a series of algorithms such as Invariant Risk Minimization (IRM) have been proposed for domain generalization.
Externí odkaz:
http://arxiv.org/abs/2311.00966
Depth-from-defocus (DFD), modeling the relationship between depth and defocus pattern in images, has demonstrated promising performance in depth estimation. Recently, several self-supervised works try to overcome the difficulties in acquiring accurat
Externí odkaz:
http://arxiv.org/abs/2303.10752
Domain generalization asks for models trained over a set of training environments to perform well in unseen test environments. Recently, a series of algorithms such as Invariant Risk Minimization (IRM) has been proposed for domain generalization. How
Externí odkaz:
http://arxiv.org/abs/2201.12919
Publikováno v:
World Electric Vehicle Journal; Feb2024, Vol. 15 Issue 2, p44, 14p
