May 30, 2018 · In this paper we present an approach which aims at learning classification models able to generalize to unseen scenarios.
Feb 7, 2018 · To overcome this issue, in this paper we present an approach which aims at learning classification models able to generalize to unseen scenarios ...
Oct 22, 2024 · ... DGSS aims to train deep neural networks that perform well on semantic segmentation tasks across multiple unseen domains. Existing DGSS ...
This paper proposes a novel deep learning framework for domain generalization that exploits recent advances in deep domain adaptation and design a ...
View recent discussion. Abstract: Traditional place categorization approaches in robot vision assume that training and test images have similar visual ...
Dec 12, 2024 · Traditional place categorization approaches in robot vision assume that training and test images have similar visual appearance.
Robust Place Categorization with Deep Domain Generalization. M Mancini, SR Bulo, B Caputo, E Ricci. IEEE Robotics and Automation Letters 3 (3), 2093-2100, 2018.
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A collection of papers on domain generalization, domain adaptation, causality, robustness, prompt, optimization, generative model, etc, organized by yfzhang114.
In this paper, we propose DomainFree Domain Generalization (DFDG), a model-agnostic method to achieve better generalization performance on the unseen test ...
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Feb 25, 2024 · We propose that deep models invariant to certain transformations should also generalize well to out-of-distribution data, i.e., they should ...