Different distances on symmetrical domains in complex space.
problem Comparing distances on specific complex domains.
method Examined Carathéodory pseudo-distance and Kähler-Einstein metric distances.
result Found the distances differ on certain complex domains.
We compute the leading and sub-leading terms in the asymptotic expansion of the Szegö kernel on the diagonal of a class of pseudoconvex Reinhardt domains whose boundaries are endowed with a general class of smooth measures. We do so by relating it to a Bergman kernel over projective space.
We show the following symmetry property of a bounded Reinhardt domain Ω in Cn+1: let M=∂Ω be the smooth boundary of Ω and let h be the Second Fundamental Form of M; if the coefficient h(T,T) related to the characteristic direction T is constant then M is a sphere. In Appendix we sta…
The Bergman kernel's minimal point determines domain properties.
problem Characterizing domains based on the Bergman kernel's minimal point.
method Using Suita conjecture and properties of Bergman and Szegö kernels.
result The Bergman kernel's minimal point identifies specific domain types.
Let D be a bounded logarithmically convex complete Reinhardt domain in Cn centered at the origin. Generalizing a result for the one-dimensional case of the unit disk, we prove that the C∗-algebra generated by Toeplitz operators with bounded measurable separately radial symbols (i.e., symbols depending …
Study on contact forms with constant curvature on CR manifolds.
problem Existence of non-homothetic contact forms with constant Tanaka-Webster scalar curvature.
method Analysis of universal covers and profinite completions of CR manifolds.
result Existence of infinitely many non-homothetic contact forms on compact CR manifolds.
Method generates intermediate domains to align source and target domains.
problem Challenges of domain adaptation with significant domain divergence.
method Progressive domain augmentation via domain interpolation and multiple subspace alignment.
result Achieves state-of-the-art performance on multiple domain adaptation tasks.
Proposes a model to improve multi-domain recommender systems.
problem Challenges in transferring knowledge between domains in recommender systems.
method Generative adversarial networks (GANs), Variational Autoencoders (VAEs), and Cycle-Consistency (CC) for weight-sharing.
result Improves performance of multi-domain recommender systems by capturing both similarities and differences among domains.
D2V learns domain-specific embeddings for domain generalization.
problem Learning decision functions across multiple related domains with limited labeled data.
method Proposes a neural network architecture, Domain2Vec (D2V), that learns domain-specific embeddings and uses them for generalization.
result D2V outperforms other algorithms in domain generalization tasks for image classification.
CUDA CTDR tackles unsupervised domain adaptation without domain alignment.
problem Lack of direct methods for unlabeled target domain classification.
method Jointly learns CTDR on source and target distributions using contradistinguish loss and supervised loss.
result CUDA CTDR achieves state-of-the-art results on various domain adaptation datasets.
Aims to eliminate domain bias in authentication without domain labels.
problem Authentication models are biased due to domain differences.
method Discover latent domains and eliminate domain difference alternately, using a meta-learning framework.
result Eliminates domain difference in authentication without domain labels.
Method infers domain-specific models without domain semantic descriptors.
problem Poor performance of standard supervised learning methods in unseen domains.
method Introduces latent domain vectors and neural networks for optimization.
result Inference of appropriate domain-specific models without semantic descriptors.
CoDAG combines domain adaptation and generalization for unsupervised continual domain shift learning.
problem Acquiring knowledge in unsupervised continual domain shift learning.
method Complementary Domain Adaptation and Generalization (CoDAG) framework.
result CoDAG outperforms state-of-the-art models in all datasets and evaluation metrics.
DCASE 2022 Task 2 tackles domain shifts in ASD for machine condition monitoring.
problem Domain shifts change acoustic characteristics, affecting ASD performance.
method Domain generalization techniques to detect anomalies across unknown domains.
result Two types of domain generalization techniques were identified and analyzed.
Extends polydisk theorem to Hartogs domains over symmetric domains.
problem Rigidity phenomena in Riemannian manifolds.
method Extension of polydisk theorem to Hartogs domains over arbitrary symmetric domains.
result Dual of a Hartogs domain over a bounded symmetric domain admits no totally geodesic immersion into any compact Riemannian manifold.
Adaptive multi-domain learning reduces parameter count for efficient deep learning.
problem Different domains have varying complexity, leading to inefficient model training.
method Proposes adaptive parameterization to reduce model complexity without sacrificing performance.
result Efficient multi-domain learning solutions with far fewer parameters.
Proposes novel losses for fine-grained categorical domain adaptation.
problem Fine-grained alignment of categories across domains in unsupervised domain adaptation.
method Joint category-domain classifier with adversarial training losses for both domain and category levels, and vicinal domain adaptation.
result Achieves state-of-the-art performance on benchmark datasets.
Improves unsupervised domain adaptation by mixing source and target domains.
problem Improves unsupervised domain adaptation by mixing source and target domains.
method Enforces training constraints across domains using mixup formulation and feature-level consistency regularizer.
result Significantly improves state-of-the-art performance on image classification and human activity recognition tasks.
New approach tackles open compound domain adaptation without clear domain labels.
problem Adapting models to new, mixed domains without domain labels.
method Curriculum domain adaptation strategy and memory module.
result Demonstrated effectiveness on various tasks.
MetFA aligns source and target domains for cross-device image classification.
problem Learning discriminative class boundaries across different domains.
method Distance metric guided feature alignment (MetFA) for domain-invariant and discriminative feature extraction.
result MetFA outperforms state-of-the-art methods in cross-device image classification.
CoNDA improves domain classification for IPDAs by incorporating new and personalized domains.
problem Continuous learning for new and personalized domains in IPDAs.
method Neural network based approach for incremental learning.
result CoNDA achieves high accuracy and outperforms baselines.
CSD learns a common component for domain generalization, outperforming existing methods.
problem Training models to generalize across unseen domains.
method CSD decomposes the model into a common and specific component, discarding the latter.
result CSD outperforms state-of-the-art domain generalization methods.
Method learns domain-specific representations without supervision.
problem Domain generalization without labeled data.
method Hierarchical variational autoencoder approach.
result Model generalizes to unseen domains without domain supervision.
A new method uses normalizing flows for gradual domain adaptation.
problem Difficulty in domain adaptation when source and target domains have a large gap.
method Proposes using normalizing flows to learn a transformation from target to Gaussian mixture distribution.
result Improves classification performance and mitigates the problem of gradual self-training failure.
Study shows how many domains are needed for generalization, using a new measure called domain shattering dimension.
problem How many domains are needed for domain generalization?
method Introduced a new combinatorial measure called the domain shattering dimension to model domain sample complexity.
result Established a tight quantitative relationship between domain shattering dimension and classic VC dimension.
TAROT enhances robustness and domain adaptability with domain-invariant features.
problem Developing models robust to adversarial attacks across diverse domains.
method Derives a new generalization bound and proposes TAROT algorithm.
result TAROT outperforms state-of-the-art methods in accuracy and robustness.
A new method improves cross-domain sentiment analysis by learning weighted domain-invariant representations.
problem Label distribution changes across domains harm domain adaptation in DIRL.
method Proposes WDIRL, a modification to DIRL that learns weighted domain-invariant representations.
result Empirical studies show the effectiveness of WDIRL in cross-domain sentiment analysis.
A new model for imputing missing values in time series data across domains.
problem Imputing missing values in time series data across domains with domain shifts and high missing rates.
method A diffusion-based imputation model that integrates shared spectral components and domain-specific temporal structures, with cross-domain consistency alignment.
result Our model effectively handles missing values and domain shifts, outperforming existing methods.
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.
problem Tackles the challenge of selecting source instances for positive transfer in partial domain adaptation.
method Proposes a Domain Adversarial Reinforcement Learning (DARL) framework that uses deep Q-learning and domain adversarial learning to select source instances and learn domain-invariant features.
result Demonstrates superior performance over existing methods for partial domain adaptation on several benchmark datasets.
Graph-Relational Domain Adaptation (GRDA) adapts domains based on their graph structure.
problem Uniform alignment of domains ignores topological structures.
method Uses a domain graph to encode adjacency and a novel graph discriminator.
result Empirically shows improved generalization and domain information incorporation.
A cost-effective framework for gradual domain adaptation using multifidelity.
problem Degrading prediction performance due to large domain distance.
method Combines multifidelity and active domain adaptation.
result Improves prediction performance with reduced sample cost.
Novel method adapts MRI brain images across multiple domains.
problem Generalization failure in medical image learning across different acquisition parameters.
method Consistency loss combined with adversarial learning.
result Significantly outperforms other domain adaptation methods in MRI lesion segmentation.
New method learns domain-invariant features for unseen domains.
problem Learning models that generalize to unseen domains with different statistics.
method Learning to learn approach, training a domain-invariant feature extractor.
result Method outperforms state-of-the-art solutions in both domain generalization and heterogeneous domain generalization.
Proposes joint domain alignment and discriminative feature learning for deep domain adaptation.
problem Reduces domain shift and misclassification of target domain samples.
method Instance-based and center-based discriminative feature learning methods.
result Learning discriminative features in shared feature space significantly boosts deep domain adaptation performance.
Proposes first method for continuously indexed domain adaptation.
problem Challenges of transferring knowledge between continuously indexed domains.
method Combines adversarial adaptation with a novel discriminator.
result Outperforms state-of-the-art methods on synthetic and real-world datasets.
Dynamic residual adapters improve performance across multiple latent domains without domain labels.
problem Overfitting to large domains and ignoring smaller ones in multi-domain learning.
method Dynamic residual adapters and augmentation strategies inspired by style transfer.
result Dynamic residual adapters significantly outperform standard models on multiple latent domains.
LRS-DAG improves domain adaptation for low-resource settings.
problem Maintaining performance on source domain after target domain adaptation.
method Adds encoder layers to map target to source domain, maintaining source performance.
result Outperforms fine-tuning on synthetic low-resource datasets.
New method removes pseudo-label bias for unsupervised domain adaptation.
problem Class imbalance and distribution shift between domains.
method Implicit class-conditioned domain alignment without explicit pseudo-label optimization.
result Effective in handling within-domain class imbalance and between-domain class distribution shift.
Paper proposes MDAT to stabilize domain alignment in label-scarce settings.
problem Stable and comprehensive domain alignment in label-scarce settings.
method Max-margin Domain-Adversarial Training (MDAT) with Adversarial Reconstruction Network (ARN).
result MDAT stabilizes gradient reversing and achieves strong robustness to hyper-parameters.
JSCN improves cross-domain recommendation by learning domain-invariant user representations.
problem Cross-domain recommendation data sparsity and domain-incompatibility issues.
method JSCN uses multi-layer spectral convolutions on different graphs to learn domain-invariant user representations and domain adaptive user mappings.
result Significant improvement in cross-domain recommendation performance (9.2% recall, 36.4% MAP improvements).
Paper tackles robust domain adaptation without target domain data.
problem Learning domain invariant representations without target domain data.
method Integrates deep autoencoder and causal structure learning into a unified model.
result CAE learns causal representations using only source domain data.
Interventional domain adaptation improves feature transferability by removing spurious correlations.
problem Improper feature transferability due to spurious correlations in domain adaptation.
method Intervention strategy using unlabeled target data to generate counterfactual features and train discriminability invariance.
result Consistent performance improvements over state-of-the-art approaches in various domain adaptation tasks.
A new CNN architecture tackles domain shifts with a dictionary approach.
problem Handling domain shifts in deep learning models.
method Decompose CNN layers into domain-specific and shared parts using a dictionary of atoms.
result The approach promotes shared semantics across domains with minimal additional parameters.
New criterion improves domain adaptation performance.
problem Binary classification in a target domain with unlabeled data and domain shift.
method Introduces a generalized Neyman-Pearson criterion for optimal domain adaptation.
result Stronger domain adaptation results possible with new criterion.
TLR learns better latent representations for unsupervised domain adaptation.
problem Learning models in a target domain using data from a source domain.
method Designing a simple linear autoencoder objective function to derive robust latent representations.
result TLR reduces domain shift and preserves common properties of both domains.
DSSM separates domain-invariant dynamics from domain-specifics in sequential data.
problem Learning cross-domain sequence representations from diverse data domains.
method Introduce disentangled state space models (DSSM) using unsupervised VAE-based training.
result Improves knowledge transfer and robust prediction across domains.