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Found 4 papers, 2 papers with code
Domain penalisation for improved Out-of-Distribution Generalisation
We also compare the performance of our approach on FCOS detector and show that our approach improves the baseline OOD performance over the existing approaches by 1. 3 percent and 1. 4 percent on validation and test sets, respectively.
Autoencoder based approach for the mitigation of spurious correlations
Deep neural networks (DNNs) have exhibited remarkable performance across various tasks, yet their susceptibility to spurious correlations poses a significant challenge for out-of-distribution (OOD) generalization.
Optimized Custom Dataset for Efficient Detection of Underwater Trash
Accurately quantifying and removing submerged underwater waste plays a crucial role in safeguarding marine life and preserving the environment.
Domain Generalisation for Object Detection under Covariate and Concept Shift
Domain generalisation aims to promote the learning of domain-invariant features while suppressing domain-specific features, so that a model can generalise better to previously unseen target domains.
