This work examines how embedding complexity impacts domain adaptation.
problem Improving generalization to an unlabeled target domain.
method Theoretical and empirical study of embedding complexity in multilayer neural networks.
result Developed a strategy to mitigate embedding complexity sensitivity and achieve performance on par with best tradeoffs.
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.
Learning domain-invariant representation is a dominant approach for domain generalization (DG), where we need to build a classifier that is robust toward domain shifts. However, previous domain-invariance-based methods overlooked the underlying dependency of classes on domains, which is responsible for the trade-off be…
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.
Estimates model performance under distribution shift using domain-invariant predictors.
problem Poor performance of models on test distributions different from training distributions.
method Uses domain-invariant predictors as a proxy for unknown target labels.
result Shows that the complexity of latent representations influences target risk.
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.
Paper develops upper-bounds for target general loss in multiple source DA and DG settings.
problem Complexity and trade-offs in multiple source domain adaptation and domain generalization.
method Defines two types of domain-invariant representations and studies their pros, cons, and trade-offs.
result Developed upper-bounds for target general loss offer insights into domain-invariant representations.
Unified deep architecture for domain-invariant network alignment.
problem Eliminate domain representation bias in network alignment.
method DANA (Domain Adversarial Network Alignment) using graph convolutional networks and semi-supervised learning.
result Achieves state-of-the-art alignment results on real-world social networks.
Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to learn domain invariant feature representations while the learned representations sh…
The performance of automatic speech recognition (ASR) systems can be significantly compromised by previously unseen conditions, which is typically due to a mismatch between training and testing distributions. In this paper, we address robustness by studying domain invariant features, such that domain information become…
Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network …
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.
Learning domain-invariant representations has become a popular approach to unsupervised domain adaptation and is often justified by invoking a particular suite of theoretical results. We argue that there are two significant flaws in such arguments. First, the results in question hold only for a fixed representation and…
Proposes a graph embedding framework for domain adaptation.
problem Improving performance on small datasets using related large datasets.
method Formulates domain adaptation as a graph embedding problem, learning a feature transformation end-to-end.
result Simple instantiation leads to state-of-the-art performance on benchmarks.
DIVA learns domain-invariant latent subspaces for domain generalization.
problem Learning representations across multiple domains for unseen data.
method Domain Invariant Variational Autoencoder (DIVA) with three latent subspaces.
result DIVA improves domain generalization performance and incorporates unlabeled data effectively.
Harmonization schemes limit accuracy due to domain information.
problem Harmonization schemes lead to inaccurate predictions due to domain information.
method Analysis of mutual information and real label value informativeness.
result Accuracy is limited by the domain with least information.
New approach tackles domain adaptation without assuming domain invariant representations.
problem Learning models on source domains for target domains with labeled and unlabeled data.
method Hidden Covariate Shift hypothesis; learning representation to match joint distributions.
result State-of-the-art performance on Amazon Reviews dataset.
The paper proposes a method to create domain-invariant representations using Wasserstein distance.
problem Domain shifts in training data affect machine learning model performance across different domains.
method The method combines classification/regression losses with a GAN-type discriminator to minimize the Wasserstein distance between domains.
result The approach produces the highest minimum classification accuracy and most invariant representation across domains.
Unsupervised domain adaptation methods aim to alleviate performance degradation caused by domain-shift by learning domain-invariant representations. Existing deep domain adaptation methods focus on holistic feature alignment by matching source and target holistic feature distributions, without considering local feature…
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.
The paper proposes using a discriminator for both domain adaptation and pseudo labeling confidence.
problem Improving generalization of classifiers trained on labeled source data to unlabeled target data.
method Multi-purposing the discriminator to learn domain-invariant feature representations and generate pseudo labels based on confidence.
result The approach enhances classifier performance by providing confidence measures for pseudo labels.
End-to-end DA method for domain-invariant CNNs using parallel audio recordings.
problem Distribution mismatches between training and application data in machine listening.
method Enforcing equal hidden layer representations for domain-parallel samples.
result Learn domain-invariant classifiers without requiring classification labels.
Paper tackles domain invariant sentiment classification using weak supervision.
problem Learning a sentiment classification model that adapts to any target domain.
method Two-stage training procedure with weakly supervised datasets.
result Transfer learning with weak supervision achieves performance close to supervised training.
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).
MDA learns domain-invariant features for better target domain classification.
problem Improving model performance on unseen target domains using multiple source domains.
method MDA learns a domain-invariant feature transformation with minimal divergence, maximal separability, and compactness.
result MDA achieves better generalization on unseen target domains compared to existing methods.
Discrepancy between training and testing domains is a fundamental problem in the generalization of machine learning techniques. Recently, several approaches have been proposed to learn domain invariant feature representations through adversarial deep learning. However, label shift, where the percentage of data in each …
A new method improves label propagation for unsupervised domain adaptation.
problem Improving unsupervised domain adaptation through semi-supervised learning techniques.
method Label Propagation with Augmented Anchors (A2LP) for UDA. result A2LP improves over representative UDA methods and benchmarks. Proposes Infomax and Domain-Independent Representations for robust causal inference.
problem Handling treatment selection bias and domain imbalance in causal inference with real-world data.
method Utilizes mutual information to learn domain-invariant representations that maximize predictive common information.
result Achieves state-of-the-art performance on causal effect inference across various data distributions.
Unified framework for multi-domain learning and data imputation.
problem Improving performance across different domains with missing data.
method Adversarial autoencoder for domain-invariant embeddings and data imputation.
result Superior performance compared to state-of-the-art methods in various settings.
We tackle class imbalance in unsupervised domain adaptation using latent codes.
problem Class imbalance in unsupervised domain adaptation where target domain has under-represented classes.
method Adversarial domain adaptation framework with latent codes to identify and estimate target labels.
result Latent codes can disentangle target domain structure and identify under-represented classes.
MTS-CycleGAN adapts multivariate time series data for ironmaking industry.
problem Creating a domain invariant dataset from multivariate time series data of different blast furnaces.
method Adversarial-based deep mapping learning network (CycleGAN) with LSTM-based AutoEncoder and discriminator.
result MTS-CycleGAN successfully translates multivariate time series data between different blast furnaces.
GENIE balances domain-invariant feature learning and gradient alignment for improved DG performance.
problem Domain Generalization (DG) overfitting to domain-specific features
method GENIE (Generalization-ENhancing Iterative Equalizer) optimizer
result Prevents a small subset of parameters from dominating optimization, promoting domain-invariant feature learning
Recent proofs of classical theorems in polynomial algebra and functional analysis are discussed, which use tools from the topology of real manifolds. Simpler proofs were discovered in the new century, of the Hilbert Nullstellensatz, and the Gelfand-Mazur Theorem. We give a related proof that an irreducible real polynom…
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.
Improves transferability of representations from source to target domains with weights and invariant representations.
problem Label shift between source and target domains in unsupervised domain adaptation.
method Integrates weights and invariant representations to bound the target risk, highlighting the role of inductive bias.
result Empirical evidence shows that weak inductive bias makes adaptation more robust.
New approach improves domain adaptation with label shift assumptions.
problem Improving domain adaptation when label distributions differ between source and target domains.
method Proposes generalized label shift (GLS) and modifies three DA algorithms (JAN, DANN, CDAN) to handle label distribution mismatches. result Modified DA algorithms outperform base versions, especially with large label distribution mismatches.
Semi-supervised method enhances recommendation from sparse data.
problem Data sparsity in implicit feedbacks and lack of negative samples.
method Domain adaptation using textual features to align latent spaces.
result Our models significantly outperform existing models on real-world datasets.
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.
Domain generalization aims to apply knowledge gained from multiple labeled source domains to unseen target domains. The main difficulty comes from the dataset bias: training data and test data have different distributions, and the training set contains heterogeneous samples from different distributions. Let X denote …
Selective pseudo-labeling improves unsupervised domain adaptation.
problem Classifying unlabeled target domain samples with labeled source domain samples.
method Structured prediction for selective pseudo-labeling.
result Selective pseudo-labeling outperforms state-of-the-art methods.
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.
This paper investigates domain generalization: How to take knowledge acquired from an arbitrary number of related domains and apply it to previously unseen domains? We propose Domain-Invariant Component Analysis (DICA), a kernel-based optimization algorithm that learns an invariant transformation by minimizing the diss…
DAAN dynamically adapts adversarial learning for better domain adaptation.
problem Dynamic evaluation of global vs local domain distributions for adversarial learning.
method Dynamic Adversarial Adaptation Network (DAAN) that dynamically learns domain-invariant representations.
result DAAN achieves better classification accuracy compared to state-of-the-art methods.
The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the discrepancy between domain-specific latent feature representations directly in the hidden activation space. Although some standard distributi…
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.
New framework tackles multi-source domain adaptation with optimism and consistency.
problem Adjusting mixture distribution weights and ensuring low error on target domain.
method Mildly optimistic objective function and consistency regularization.
result Beats current state of the art in multi-source domain adaptation.
New algorithm improves treatment effect estimation from observational data.
problem Estimating the benefits and harms of interventions from observational data.
method Develops a deep kernel regression algorithm and posterior regularization framework.
result Substantially outperforms state-of-the-art on various benchmarks datasets.
Current methods for covariate-shift adaptation use unlabelled data to compute importance weights or domain-invariant features, while the final model is trained on labelled data only. Here, we consider a particular case of covariate shift which allows us also to learn from unlabelled data, that is, combining adaptation …