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Found 8 papers, 1 papers with code
Rethinking Domain Generalization Baselines
Despite being very powerful in standard learning settings, deep learning models can be extremely brittle when deployed in scenarios different from those on which they were trained.
Ranked #54 on
Domain Generalization
on PACS
Self-Supervised Learning Across Domains
Human adaptability relies crucially on learning and merging knowledge from both supervised and unsupervised tasks: the parents point out few important concepts, but then the children fill in the gaps on their own.
Ranked #79 on
Domain Generalization
on PACS
One-Shot Unsupervised Cross-Domain Detection
Despite impressive progress in object detection over the last years, it is still an open challenge to reliably detect objects across visual domains.
Learning to Generalize One Sample at a Time with Self-Supervision
Although deep networks have significantly increased the performance of visual recognition methods, it is still challenging to achieve the robustness across visual domains that is necessary for real-world applications.
Tackling Partial Domain Adaptation with Self-Supervision
Domain adaptation approaches have shown promising results in reducing the marginal distribution difference among visual domains.
Domain Generalization by Solving Jigsaw Puzzles
Human adaptability relies crucially on the ability to learn and merge knowledge both from supervised and unsupervised learning: the parents point out few important concepts, but then the children fill in the gaps on their own.
Ranked #3 on
Domain Generalization
on NICO Animal
Domain Generalization with Domain-Specific Aggregation Modules
Visual recognition systems are meant to work in the real world.
Ranked #82 on
Domain Generalization
on PACS
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition
Despite the impressive progress brought by deep network in visual object recognition, robot vision is still far from being a solved problem.
