Results 21 to 30 of about 103,215 (258)
Evolving Domain Generalization
CoRR, 2022 Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data. Existing domain generalization methods ignore the relationship between tasks, implicitly assuming that all the tasks are sampled from a stationary environment ...
William Wei Wang +8 more
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Domain Generalization with MixStyle
CoRR, 2021 Though convolutional neural networks (CNNs) have demonstrated remarkable ability in learning discriminative features, they often generalize poorly to unseen domains. Domain generalization aims to address this problem by learning from a set of source domains a model that is generalizable to any unseen domain.
Kaiyang Zhou +3 more
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Learning Robust Shape-Based Features for Domain Generalization
IEEE Access, 2020 Domain generalization is a challenging problem of learning models that can generalize to novel testing domains which are unavailable during training and follow different distributions from training domains.
Yexun Zhang +3 more
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Multi-Domain Feature Alignment for Face Anti-Spoofing
Sensors, 2023 Face anti-spoofing is critical for enhancing the robustness of face recognition systems against presentation attacks. Existing methods predominantly rely on binary classification tasks.
Shizhe Zhang, Wenhui Nie
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Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limited source domains in a static model. Unfortunately, there is a lack of training-free mechanism to adjust the model when generalized to the agnostic target domains.
Zhishu Sun +6 more
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Barycentric-Alignment and Reconstruction Loss Minimization for Domain Generalization
IEEE Access, 2023 This paper advances the theory and practice of Domain Generalization (DG) in machine learning. We consider the typical DG setting where the hypothesis is composed of a representation mapping followed by a labeling function.
Boyang Lyu +4 more
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Domain Generalization for Domain-Linked Classes
CoRR, 2023 Domain generalization (DG) focuses on transferring domain-invariant knowledge from multiple source domains (available at train time) to an, a priori, unseen target domain(s). This requires a class to be expressed in multiple domains for the learning algorithm to break the spurious correlations between domain and class.
Kimathi Kaai +2 more
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Zero-Shot Domain Generalization [PDF]
Proceedings of the British Machine Vision Conference 2020, 2020 Standard supervised learning setting assumes that training data and test data come from the same distribution (domain). Domain generalization (DG) methods try to learn a model that when trained on data from multiple domains, would generalize to a new unseen domain.
Udit Maniyar +4 more
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Duplications and domain-generality. [PDF]
Psychological Bulletin, 2019 Although specialized, adaptive behavioral traits are ubiquitous in the animal kingdom, at least in humans, there are considerable debates on whether the mind is primarily characterized by various special-purpose, domain-specific mechanisms or by a few general-purpose, domain-general mechanisms.
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