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.
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.
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…
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).
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.
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.
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.
New findings on representation changes in transfer learning.
problem Understanding representation changes in transfer learning.
method Analysis of pre-trained and fine-tuned representations in transfer learning.
result Representation changes mainly occur in the head, but early layers are also important for cross-domain adaptation.
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.
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.
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.
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.
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.
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.
This paper generalizes graph representation for diverse data types.
problem Representing and querying hybrid data types in a unified way.
method Introducing a directed Tensor-Typed Multi-Graph with embeddings.
result Unified representation for visual, linguistic, and auditory data.
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.
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.
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.
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.
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.
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.
A new method for few-shot classification across multiple domains.
problem Few-shot classification in multiple domains.
method Cross-domain meta-learning with a pool of modulated models.
result Improved few-shot classification performance across diverse domains.
URT layer improves few-shot image classification across diverse domains.
problem Few-shot image classification in multi-domain settings.
method Meta-learns to dynamically re-weight and compose domain-specific representations.
result Sets new state-of-the-art on Meta-Dataset.
Two semi-supervised manifold alignment methods improve cross-domain classification.
problem Aligning data from multiple sources for better analysis.
method SPUD and MASH methods using graph integration and diffusion.
result SPUD and MASH methods outperform existing methods in cross-domain classification.
Cross-domain visual data matching is one of the fundamental problems in many real-world vision tasks, e.g., matching persons across ID photos and surveillance videos. Conventional approaches to this problem usually involves two steps: i) projecting samples from different domains into a common space, and ii) computing (…
TCRI improves domain generalization by enforcing conditional independence constraints.
problem Limitations of existing domain generalization methods due to incomplete constraints.
method TCRI implements regularizers motivated by conditional independence constraints.
result TCRI achieves cross-domain stability and outperforms baselines in worst-domain accuracy.
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.
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.
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.
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.
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.
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.
New model learns coupled representations for domains, intents, and slots.
problem Representation learning for domains, intents, and slots in spoken language understanding.
method Proposes a model that learns coupled representations by aggregating slot and intent representations based on their hierarchical relationships.
result Improved performance on contextual cross-domain reranking task.
Few-shot cell segmentation from diverse sources to target domain.
problem Efficient cell segmentation from limited annotated images.
method Meta-learning combining cross-domain tasks and invariant representation.
result Promising results from 1-10-shot learning on public databases.
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.
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.
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.
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.
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…
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.
This research shows how to learn shared representations from unpaired data.
problem Learning shared representations from unpaired data.
method Spectral embeddings of random walk matrices from each unimodal representation.
result Shared representations can be learned almost exclusively from unpaired data.
Single auto-encoder learns cross-domain image translation.
problem Cross-domain image-to-image translation using a single encoder-decoder architecture.
method Single auto-encoder with independent domain and content encodings.
result Cross-domain mapping achieved without separate encoders.
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.
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.
CDSPP learns domain-specific projections for heterogeneous domain adaptation.
problem Heterogeneous domain adaptation problems where source and target domains have different modalities or feature dimensions.
method Cross-Domain Structure Preserving Projection (CDSPP) algorithm that learns domain-specific projections to map features into a common subspace.
result CDSPP achieves superior performance in both supervised and semi-supervised HDA compared to state-of-the-art methods.
BOIL updates model body only, showing better few-shot learning performance.
problem Few-shot learning efficiency with model reuse vs. change.
method Proposes BOIL, updating only model body, freezing head.
result Significantly outperforms MAML on cross-domain tasks.
This paper proposes learning priors for adversarial autoencoders to improve model expressiveness.
problem The choice of priors in deep latent factor models can significantly affect model expressiveness, especially for models with limited capacity.
method The authors introduce code generators to transform simple priors into ones that better characterize the data distribution for adversarial autoencoders.
result The proposed model generates better image quality and learns better disentangled representations than standard AAEs in supervised and unsupervised settings.