Paper presents a new method for cross-domain visual matching.
problem Cross-domain visual data matching in real-world vision tasks.
method Expands linear projections into affine transformations and combines Mahalanobis distance and Cosine similarity.
result Superior performance in cross-domain matching tasks over state-of-the-art methods.
Deep neural networks improve face matching across different domains for banking security.
problem Matching facial images from ID documents with selfies for secure transactions.
method A novel deep learning architecture using two CNNs for cross-domain face matching.
result Accuracy rates higher than 93% on the FaceBank dataset.
MWGAN tackles multi-marginal matching problem with Wasserstein GAN.
problem Learning mappings to match a source domain to multiple target domains with cross-domain correlations.
method Develops a novel Multi-marginal Wasserstein GAN (MWGAN) with inner- and inter-domain constraints to minimize Wasserstein distance.
result Theoretical and empirical evaluations show MWGAN's effectiveness on balanced and imbalanced translation tasks.
The goal of cross-domain object matching (CDOM) is to find correspondence between two sets of objects in different domains in an unsupervised way. Photo album summarization is a typical application of CDOM, where photos are automatically aligned into a designed frame expressed in the Cartesian coordinate system. CDOM i…
DMAE learns shared latent space from unpaired data.
problem Learning shared latent space from unpaired multi-modal data.
method Formulates cross-domain representation learning and object matching problem, optimizes autoencoders and pairing.
result Promising results in image captioning and unsupervised classifier learning.
Data vectors are obtained from multiple domains. They are feature vectors of images or vector representations of words. Domains may have different numbers of data vectors with different dimensions. These data vectors from multiple domains are projected to a common space by linear transformations in order to search clos…
L2M learns to match distributions for domain adaptation without relying on hand-crafted priors.
problem Difficult to directly match cross-domain distributions; existing methods fail to uncover underlying relationships.
method Learning to Match (L2M) framework that learns distribution matching loss using a meta-network in a data-driven way.
result L2M outperforms state-of-the-art methods on public datasets and transfers from pneumonia to COVID-19 images.
Develops Triangle GAN for semi-supervised cross-domain learning.
problem Semi-supervised cross-domain joint distribution matching with limited labeled data.
method Triangle GAN architecture with two generators and two discriminators trained adversarially.
result Generators learn conditional distributions between domains, discriminators define ternary function.
Superhighway bypasses data sparsity in cross-domain CF.
problem Data sparsity in cross-domain collaborative filtering.
method Explicit relation-enrichment procedure to enhance cross-domain connectivity.
result Significantly improves recommendation performance in both target and source domains.
A new model CDT improves cross-domain recommendation by learning translation vectors.
problem Improving cross-domain recommendation by transferring user preferences across different platforms.
method Translation-based learning approach with embedding space and factorization machines.
result The proposed CDT model outperforms other cross-domain strategies in six publicly available tasks.
Sparse coding has shown its power as an effective data representation method. However, up to now, all the sparse coding approaches are limited within the single domain learning problem. In this paper, we extend the sparse coding to cross domain learning problem, which tries to learn from a source domain to a target dom…
Proposes ADC for cross-domain recommendation balancing user preferences.
problem Users' preferences change across different domains (e.g., social media, e-commerce).
method Designs a neural architecture and cross-domain loss function to adaptively balance user preferences.
result ADC model effectively balances the impact of domains with different complexities.
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).
Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains. However, previous models only assume that multiple domains share a latent common rating pattern based on the use…
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.
The strength of association between a pair of data vectors is represented by a nonnegative real number, called matching weight. For dimensionality reduction, we consider a linear transformation of data vectors, and define a matching error as the weighted sum of squared distances between transformed vectors with respect…
ADGAN improves risk tolerance prediction by aligning cross-domain data.
problem Lack of professional knowledge and domain-specific models in risk tolerance studies.
method Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale inequality.
result ADGAN better handles class imbalance and unqualified data than state-of-the-art methods.
A new method transfers data between domains using a common space and adaptive functions.
problem Domain transfer learning between different datasets.
method Mapping data to a common space, learning a classifier there, and adapting it for each domain.
result The method outperforms existing transfer learning methods on benchmark datasets.
NeuCDCF combines matrix factorization and deep learning for cross-domain recommendation.
problem Data sparsity and cold-start problems in recommendation systems.
method Wide and deep framework combining matrix factorization and deep neural networks.
result NeuCDCF outperforms state-of-the-art models on real-world datasets.
AdaFlow adapts DNN density estimators to new distributions for anomaly detection and cross-domain translation.
problem Unsupervised anomaly detection and cross-domain translation with limited paired data.
method AdaFlow combines Normalizing Flows and Adaptive Batch-Normalizations for domain adaptation.
result AdaFlow efficiently adapts to new distributions with minimal computational resources.
HAGAN uses hierarchical attention to improve cross-domain sentiment classification.
problem Cross-domain sentiment classification with domain discrepancy.
method Hierarchical attention in GANs to produce domain-indistinguishable document representations.
result HAGAN outperforms existing methods on Amazon review dataset.
The paper provides a risk bound for unsupervised cross-domain mapping algorithms.
problem Theoretical support for unsupervised cross-domain mapping algorithms is lacking.
method Adversarial training methods based on IPMs and a novel risk bound.
result A risk bound that upper bounds the risk between learned and target mappings.
DARec adapts rating patterns across domains without auxillary info.
problem Cross-domain recommendation challenges.
method Deep domain adaptation model (DARec) that transfers rating patterns.
result Best performance on public datasets.
GWIL uses Gromov-Wasserstein distance to align expert and imitation agent states.
problem Cross-domain imitation learning challenges due to different system dimensions and stationary distributions.
method Gromov-Wasserstein Imitation Learning (GWIL) using Gromov-Wasserstein distance.
result GWIL effectively aligns expert and imitation agent states in various continuous control domains.
DMGE learns cross-domain user behavior embeddings using multi-graphs and GNNs.
problem Data sparsity in learning large-scale item embedding from individual domain data.
method Construct multi-graphs from users' behaviors across domains, use multi-graph neural networks to learn cross-domain representation.
result DMGE outperforms state-of-the-art embedding methods in various tasks.
This paper improves cross-domain learning using random forests for manifold alignment.
problem Improving cross-domain learning and feature integration.
method Semi-supervised manifold alignment using random forest proximities.
result Random forest proximities enhance downstream classification accuracy.
PHINN: A generative model for rare-event time series using persistent homology
problem Generating rare events in time series
method Flow-matching framework with dynamic Betti curves and persistence landscape loss
result Outperforms statistical and diffusion baselines in topological fidelity and tail coverage
Study shows cross-domain X-ray prediction performance discrepancies and label shifts.
problem Quantifying generalization limits across different X-ray datasets.
method Large-scale study on multiple X-ray datasets, focusing on performance and label shifts.
result Interesting discrepancies found between model performance and agreement, and concept similarity across tasks.
CosML combines domain-specific meta-learners for cross-domain few-shot classification.
problem Generalizing to unseen domains while meta-learning on multiple seen domains.
method CosML trains domain-specific meta-learners and combines their meta-parameters in the parameter space.
result CosML outperforms state-of-the-art methods and achieves strong cross-domain generalization.
Proposes DT-LET for better transfer learning between domains with different resolutions.
problem Transfer learning between domains with different resolutions often fails due to mismatched hidden layers.
method Develops DT-LET model to find the best matching layers for transfer based on high-level feature correspondence.
result DT-LET achieves superior results in cross-domain recognition/classification tasks, validating the necessity of layer correspondence searching.
CoNet uses neural networks to transfer knowledge across domains for better recommendations.
problem Data sparsity in cross-domain recommendation systems.
method CoNet combines neural networks and cross mappings to transfer knowledge between domains.
result CoNet outperforms baseline methods by 7.84% in NDCG.
Dual-structured method improves cross-domain imitation learning.
problem Learning policies in a target domain from source domain expert demonstrations.
method Extracts domain and policy expertness features, synthesizes new features.
result Superior performance compared to other algorithms in MuJoCo tasks.
New approach improves cross-domain recommendation for sparse target domains.
problem Cross-domain recommendation challenges with sparse target domains.
method Guided neural collaborative filtering with domain-invariant components across dense and sparse domains.
result Effective and scalable approach demonstrated on public and Visa datasets.
Novel methods robustify Gromov-Wasserstein distance for cross-domain alignment.
problem Robustifying Gromov-Wasserstein distance for cross-domain alignment.
method Three novel techniques derived from robust statistics to improve GW and its variants.
result Empirical validation shows superior resilience to contamination.
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.
Meta learning enables cross-domain Hamiltonian dynamics.
problem Adapting to new physical systems with different laws.
method Graph Neural Network (GNN) with meta learning.
result Unified Hamiltonian representation across multiple system domains.
Paper proposes LMM-PQS for cross-domain few-shot learning.
problem Cross-domain few-shot learning problem.
method Generates pseudo query images and fine-tunes feature extraction modules with a large margin mechanism.
result LMM-PQS outperforms baseline models in cross-domain few-shot learning.
A new method combines multiple bounds and betting strategies for selective prediction, improving risk coverage in data-scarce settings.
problem Selective prediction with risk control in data-scarce domains.
method Combines concentration inequalities, multiple-testing corrections, and betting-based confidence sequences.
result Transfer-Informed Betting achieves tighter bounds and better coverage in data-scarce settings.
This paper tackles UDA by learning domain-invariant embeddings using distribution alignment and pseudo-labels.
problem Unsupervised domain adaptation between two visual domains.
method Shared deep encoder, Sliced-Wasserstein Distance, deep classifier, pseudo-labels for class alignment.
result Effective solution for training deep classification networks on source domain to generalize to target domain.
New RL approach transfers policies across related domains.
problem Efficiently transfer policies between different domains in RL.
method Adapts and reuses optimal policies from related source tasks.
result Improves sample efficiency in target domain learning.
Proposes IFCDA framework to improve cross-domain adaptation.
problem Negative transfer and difficulty in handling category-irrelevant losses in DA.
method Importance filtered mechanism to generate filtered soft labels, combined with graph-based label propagation.
result Significantly improves performance in both Closed-Set and Open-Set DA scenarios.
VASE learns disentangled representations that generalize across domains.
problem Learning new knowledge from diverse data sources while preserving old knowledge.
method VASE uses shared embeddings and Minimum Description Length principle to disentangle representations.
result VASE achieves better cross-domain inference and disentangled representations.
Proposes a new method for manifold alignment using geometry-regularized twin autoencoders.
problem Traditional MA methods lack out-of-sample extension and real-world applicability.
method Guided representation learning with geometry-regularized twin autoencoders.
result Improves cross-domain generalization and robustness while maintaining alignment fidelity.
Analyzes stock trends and e-commerce user behavior using Twitter data.
problem Understanding the relationship between stock prices, stock news, and e-commerce user behavior.
method Cross-domain analysis using Hadoop, Hive, and Tableau on three datasets.
result Identified correlations between stock sentiment, stock trends, and e-commerce user behavior.
ROCCO solves Co-Clustering problems efficiently over massive datasets.
problem Simultaneously clustering samples and features in cross-domain datasets.
method Graph-based two-sided representation learning with non-convex optimization.
result ROCCO achieves state-of-the-art performance and is robust to noise.
A new framework pretrains a single GNN model for diverse graphs, overcoming domain-specific challenges.
problem Difficulty in generalizing across graphs from different domains using existing GNNs.
method Cross-domain pretraining framework with gating functions to choose experts for new graphs.
result Superior performance on link prediction and node classification tasks across various domains.
PyDCI improves DCI method, outperforming existing techniques on text classification benchmarks.
problem Cross-domain and cross-lingual text classification challenges.
method Reimplemented DCI method in Python, using scikit-learn and SciPy stack.
result PyDCI outperforms JaDCI and new high-performing methods on benchmarks.
Framework translates unlabeled images between domains.
problem Translating unlabeled images between domains with no supervision.
method Skip-connected encoder-generator structure trained with GAN, cycle, and semantic consistency losses.
result Framework can learn semantic mappings for face images without supervised one-to-one mapping.