arXiv research

Extends Polydisk Theorem to Cartan-Hartogs domains.

The paper explores symplectic geometry of Cartan-Hartogs domains.

The Cartan-Hartogs domains are defined as a class of Hartogs type domains over irreducible bounded symmetric domains. For a Cartan-Hartogs domain $Ω^{B}(μ)$ endowed with the natural Kähler metric $g(μ),$ Zedda conjectured that the coefficient $a_2$ of the Rawnsley's $\varepsilon$-function expansion for the Cartan-Harto…

We prove the existence of a Berezin-Engliš quantization for Cartan-Hartogs domains.

Study Bergman metric on Cartan-Hartogs domains and their duals.

Inspired by the work of Z. Lu and G. Tian [21] in the compact setting, in this paper we address the problem of studying the Szegö kernel of the disk bundle over a noncompact Kähler manifold. In particular we compute the Szegö kernel of the disk bundle over a Cartan-Hartogs domain based on a bounded symmetric domain. Th…

We extend a result of Z. Feng and Z. Tu by showing that if one of the coefficients $a_j$, $2\leq j\leq n$, of Rawnlsey's epsilon function associated to a $n$-dimensional Cartan-Hartogs domain is constant, then the domain is biholomorphically equivalent to the complex hyperbolic space.

The Cartan-Hartogs domains are defined as a class of Hartogs type domains over irreducible bounded symmetric domains. The purpose of this paper is twofold. Firstly, for a Cartan-Hartogs domain $Ω^{B^{d_0}}(μ)$ endowed with the canonical metric $g(μ)$, we obtain an explicit formula for the Bergman kernel of the weighted…

In this paper we address two problems concerning a family of domains $M_Ω(μ) \subset \C^n$, called Cartan-Hartogs domains, endowed with a natural Kaehler metric $g(μ)$. The first one is determining when the metric $g(μ)$ is extremal (in the sense of Calabi), while the second one studies when the coefficient $a_2$ in th…

This paper consists of two results dealing with balanced metrics (in S. Donaldson terminology) on nonconpact complex manifolds. In the first one we describe all balanced metrics on Cartan domains. In the second one we show that the only Cartan-Hartogs domain which admits a balanced metric is the complex hyperbolic spac…

Extends polydisk theorem to Hartogs domains over symmetric domains.

The definition of balanced metrics was originally given by Donaldson in the case of a compact polarized Kähler manifold in 2001, who also established the existence of such metrics on any compact projective Kähler manifold with constant scalar curvature. Currently, the only noncompact manifolds on which balanced metrics…

Method generates intermediate domains to align source and target domains.

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.

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

Recently multi-domain recommender systems have received much attention from researchers because they can solve cold-start problem as well as support for cross-selling. However, when applying into multi-domain items, although algorithms specifically addressing a single domain have many difficulties in capturing the spec…

Proposes novel losses for fine-grained categorical domain adaptation.

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…

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

In this paper, we propose a simple model referred as Contradistinguisher (CTDR) for unsupervised domain adaptation whose objective is to jointly learn to contradistinguish on unlabeled target domain in a fully unsupervised manner along with prior knowledge acquired by supervised learning on an entirely different domain…

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

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…

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…

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

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

Proposes first method for continuously indexed domain adaptation.

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

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

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

Paper tackles robust domain adaptation without target domain data.

Interventional domain adaptation improves feature transferability by removing spurious correlations.

Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. Inspired by recent work in …

A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e.g., sunny weather) for achieving high performance on the test data in a target domain (e.g., rainy weather). Whether the target contains a single homogeneous domain or multiple heterogeneous domains, existing wor…

Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain. Recent work observe that the popular adversarial approach of learning domain-invariant features is insufficient to achieve desirable target domain …

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

Paper tackles domain adaptation for contextual bandits with sub-linear regret.

Partial domain adaptation aims to transfer knowledge from a label-rich source domain to a label-scarce target domain which relaxes the fully shared label space assumption across different domains. In this more general and practical scenario, a major challenge is how to select source instances in the shared classes acro…

Contradistinguisher learns to distinguish target domain without aligning source and target domains.

CosML combines domain-specific meta-learners for cross-domain few-shot classification.

Paper proposes a new framework for predictive optimization without training data.