arXiv research

The Fock-Bargmann-Hartogs domain $D_{n,m}(μ)$ ($μ>0$) in $\mathbf{C}^{n+m}$ is defined by the inequality $\|w\|^2<e^{-μ\|z\|^2},$ where $(z,w)\in \mathbf{C}^n\times \mathbf{C}^m$, which is an unbounded non-hyperbolic domain in $\mathbf{C}^{n+m}$. Recently, Yamamori gave an explicit formula for the Bergman kernel of the…

This paper calculates and analyzes the Kobayashi pseudometric for a specific domain.

The Fock-Bargmann-Hartogs domain $D_{n,m}(μ)$ ($μ>0$) in $\mathbb{C}^{n+m}$ is defined by the inequality $\|w\|^2<e^{-μ\|z\|^2},$ where $(z,w)\in \mathbb{C}^n\times \mathbb{C}^m$, which is an unbounded non-hyperbolic domain in $\mathbb{C}^{n+m}$. This paper introduces a Kähler metric $αg(μ;ν)$ $(α>0)$ on $D_{n,m}(μ)$, …

In this paper we study Kaehler manifolds that are strongly not relative to any projective Kaehler manifold, i.e. those Kaehler manifolds that do not share a Kaehler submanifold with any projective Kaehler manifold even when their metric is rescaled by the multiplication by a positive constant. We prove two results whic…

Method generates intermediate domains to align source and target domains.

Proposes a model to improve multi-domain recommender systems.

CUDA CTDR tackles unsupervised domain adaptation without domain alignment.

Aims to eliminate domain bias in authentication without domain labels.

CoDAG combines domain adaptation and generalization for unsupervised continual domain shift learning.

DCASE 2022 Task 2 tackles domain shifts in ASD for machine condition monitoring.

Extends polydisk theorem to Hartogs domains over symmetric domains.

Adaptive multi-domain learning reduces parameter count for efficient deep learning.

Proposes novel losses for fine-grained categorical domain adaptation.

Improves unsupervised domain adaptation by mixing source and target domains.

We address the problem of domain generalization where a decision function is learned from the data of several related domains, and the goal is to apply it on an unseen domain successfully. It is assumed that there is plenty of labeled data available in source domains (also called as training domain), but no labeled dat…

New approach tackles open compound domain adaptation without clear domain labels.

MetFA aligns source and target domains for cross-device image classification.

CoNDA improves domain classification for IPDAs by incorporating new and personalized domains.

CSD learns a common component for domain generalization, outperforming existing methods.

Method learns domain-specific representations without supervision.

A new method uses normalizing flows for gradual domain adaptation.

We propose a method to infer domain-specific models such as classifiers for unseen domains, from which no data are given in the training phase, without domain semantic descriptors. When training and test distributions are different, standard supervised learning methods perform poorly. Zero-shot domain adaptation attemp…

Study shows how many domains are needed for generalization, using a new measure called domain shattering dimension.

TAROT enhances robustness and domain adaptability with domain-invariant features.

A new method improves cross-domain sentiment analysis by learning weighted domain-invariant representations.

A new model for imputing missing values in time series data across domains.

We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using technique…

We define self-adjoint extensions of the Hodge Laplacian on Lipschitz domains in Riemannian manifolds, corresponding to either the absolute or the relative boundary condition, and examine regularity properties of these operators' domains and form domains. We obtain results valid for general Lipschitz domains, and stron…

DARL framework tackles partial domain adaptation by selecting source instances for positive transfer.

Graph-Relational Domain Adaptation (GRDA) adapts domains based on their graph structure.

A cost-effective framework for gradual domain adaptation using multifidelity.

Novel method adapts MRI brain images across multiple domains.

New method learns domain-invariant features for unseen domains.

Proposes first method for continuously indexed domain adaptation.

Dynamic residual adapters improve performance across multiple latent domains without domain labels.

LRS-DAG improves domain adaptation for low-resource settings.

New method removes pseudo-label bias for unsupervised domain adaptation.

Paper proposes MDAT to stabilize domain alignment in label-scarce settings.

JSCN improves cross-domain recommendation by learning domain-invariant user representations.

Paper tackles robust domain adaptation without target domain data.

Interventional domain adaptation improves feature transferability by removing spurious correlations.

A new CNN architecture tackles domain shifts with a dictionary approach.

New criterion improves domain adaptation performance.

DSSM separates domain-invariant dynamics from domain-specifics in sequential data.

Proposes a method to directly classify target domain samples without labeled data.

Improves unsupervised domain adaptation methods by aligning class conditional distributions.

Adaptive object detection method synthesizes target domain images from source domain images.

Proposes a novel framework for unsupervised domain adaptation using specialized batch normalization.