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.
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48 results for “Deep domain adaptation”
Proposes a novel framework for unsupervised domain adaptation using causal representations.
problem Transferability of deep model representations across domains is limited.
method Integrates causal inference into deep learning pipeline for domain-invariant feature learning.
result Demonstrates superior performance in unsupervised domain adaptation using causal representations.
We consider unsupervised domain adaptation: given labelled examples from a source domain and unlabelled examples from a related target domain, the goal is to infer the labels of target examples. Under the assumption that features from pre-trained deep neural networks are transferable across related domains, domain adap…
Fisher loss improves deep domain adaptation by learning discriminative within-class compact and between-class separable representations.
problem Improving deep domain adaptation performance by learning discriminative representations.
method Proposes a Fisher loss to learn discriminative representations that are within-class compact and between-class separable.
result Noticeable improvements in deep domain adaptation performance, e.g., 6.67% absolute improvement in mean accuracy on the Office-Home dataset.
Compared with shallow domain adaptation, recent progress in deep domain adaptation has shown that it can achieve higher predictive performance and stronger capacity to tackle structural data (e.g., image and sequential data). The underlying idea of deep domain adaptation is to bridge the gap between source and target d…
Survey explores methods to adapt deep learning models across multiple labeled domains.
problem Difficulty in obtaining labeled data for deep learning models.
method Multi-source domain adaptation (MDA) to transfer knowledge from labeled to unlabeled or sparsely labeled target domains.
result MDA methods improve performance by minimizing domain shift.
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 a novel framework for unsupervised domain adaptation using specialized batch normalization.
problem Improves unsupervised domain adaptation in deep neural networks.
method Integrates domain-specific batch normalization layers in convolutional neural networks, estimating pseudo-labels for target domain examples and learning final models with multi-task classification loss.
result Achieves state-of-the-art accuracy in standard and multi-source domain adaptation scenarios.
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.
Bayesian method matches uncertainty to adapt across domains.
problem Label distribution shift across domains degrades model performance.
method Bayesian neural network quantifies uncertainty; joint feature and label distribution matching.
result Improves model performance on domain adaptation tasks.
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.
Deep learning has recently been shown to be instrumental in the problem of domain adaptation, where the goal is to learn a model on a target domain using a similar --but not identical-- source domain. The rationale for coupling both techniques is the possibility of extracting common concepts across domains. Considering…
Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised …
Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new a…
Paper tackles medical image diagnosis with unsupervised domain adaptation.
problem Limited labeled samples and label noise in medical images.
method Collaborative Unsupervised Domain Adaptation (UDA) algorithm.
result Empirical results show superiority of the proposed method.
Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptationcs.LG
Recently, considerable effort has been devoted to deep domain adaptation in computer vision and machine learning communities. However, most of existing work only concentrates on learning shared feature representation by minimizing the distribution discrepancy across different domains. Due to the fact that all the domai…
CoDATS improves DA on time series data with weak supervision.
problem Improving domain adaptation for time series data with limited labeled data.
method CoDATS model for Time Series data, DA-WS method with weak supervision.
result Significant accuracy improvements over state-of-the-art methods.
CAT improves domain adaptation for fault diagnosis by calibrating teacher network predictions.
problem Performance drops in deep learning models when applied to different data distributions.
method CAT uses post-hoc calibration techniques to calibrate predictions of the teacher network during self-training.
result CAT achieves state-of-the-art performance on most transfer tasks in domain-adaptive IFD.
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.
Benchmark for UDA in time series classification.
problem Lack of benchmarks for unsupervised domain adaptation in time series.
method Introduces a comprehensive benchmark with new datasets and state-of-the-art neural network backbones.
result Insights into strengths and limitations of UDA methods for time series data.
Adapts deep learning models trained on simulated images for use with real images.
problem Difficulty in training deep neural networks on large amounts of experimental data.
method Adversarial domain adaptation method to mitigate domain shift between simulated and experimental image data.
result Adversarial domain adaptation successfully mitigates domain shift and improves numerical observer performance.
Large-scale labeled training datasets have enabled deep neural networks to excel on a wide range of benchmark vision tasks. However, in many applications it is prohibitively expensive or time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directl…
Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domai…
DAF uses attention sharing to adapt forecasts from abundant to scarce data.
problem Limited data for time series forecasting.
method Attention-based shared module and domain discriminator for domain adaptation.
result DAF outperforms state-of-the-art methods on various domains.
This paper examines inductive biases in deep reinforcement learning and their impact on performance.
problem The trade-off between inductive biases and performance in deep reinforcement learning.
method Investigated domain-specific components and adaptive solutions in deep reinforcement learning agents.
result Adaptive components can sometimes outperform domain-specific components, but not always.
Deep neural networks have shown promising results for various clinical prediction tasks. However, training deep networks such as those based on Recurrent Neural Networks (RNNs) requires large labeled data, significant hyper-parameter tuning effort and expertise, and high computational resources. In this work, we invest…
Approach for biosensor condition detection using domain adaptation.
problem Limited clinical data for biosensor condition detection.
method Teacher-student adaptation of deep learning models.
result Significantly outperforms other methods on synthetic and real-world datasets.
The paper proposes a uniformity regularization scheme to improve deep neural network transferability.
problem Improving deep neural network transferability and adaptation to new tasks.
method Introduces a uniformity regularization scheme to encourage high uniformity in embedding space.
result Uniformity regularization consistently offers benefits over baseline methods and achieves state-of-the-art performance in Deep Metric Learning and Meta-Learning.
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.
Practicality of generalization guarantees for unsupervised domain adaptation with neural networkscs.LG
New bounds improve generalization for deep neural networks in domain adaptation.
problem Deriving tight generalization guarantees for deep neural networks in domain adaptation.
method Combining data-dependent PAC-Bayes analysis with importance weighting.
result A simple importance weighting extension provides the tightest estimable bound.
NLE embeds labels for domain adaptation with neural networks.
problem Adapting deep neural networks with unpaired source and target domain data.
method Distill source-domain knowledge into l-vectors, soft targets for adaptation.
result 14.1% relative word error rate reduction over direct re-training.
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.
Deep learning adapts HVAC models to new buildings.
problem Adapting thermal dynamics models to new buildings with limited data.
method Deep supervised domain adaptation (DSDA) using LSTM-based Sequence to Sequence model.
result Deep supervised domain adaptation improves predictive performance over learning from scratch.
KD improves DNN performance on unseen data without hyperparameter tuning.
problem Overfitting in DNNs on unseen data.
method Knowledge distillation for semi-supervised domain adaptation.
result KD achieves significantly higher WMH dice scores than baseline and ADA.
Proposes a framework to improve domain adaptation without labeled data.
problem Improving adaptability and preserving intrinsic data structure in unsupervised domain adaptation.
method Discriminative Manifold Propagation framework using soft labels and manifold metric alignment.
result The method achieves better transferability and discriminability compared to existing approaches.
A new method trains deep neural networks for open set domain adaptation without negative open set difference.
problem Training deep neural networks for open set domain adaptation without negative open set difference.
method Proposes a new upper bound of target-domain risk, including source-domain risk, ε-open set difference (), distributional discrepancy, and constant. Uses gradient descent for source-domain risk and , and adversarial training for distributional discrepancy. Trains DNNs via minimizing the new upper bound.
result Shows state-of-the-art performance on benchmark datasets.
Typically a classifier trained on a given dataset (source domain) does not performs well if it is tested on data acquired in a different setting (target domain). This is the problem that domain adaptation (DA) tries to overcome and, while it is a well explored topic in computer vision, it is largely ignored in robotic …
A new method for unsupervised domain adaptation using manifold learning.
problem Leveraging rich source domain information to target domain without labeled data.
method Discriminative Manifold Embedding and Alignment framework.
result Consistent transferability and discriminability achieved through manifold metric alignment.
AVDA transfers knowledge from source to target domains using embeddings.
problem Transferring knowledge from a source domain to a target domain with limited labeled data.
method Adversarial Variational Domain Adaptation (AVDA) with deep embeddings and Gaussian Mixture Model.
result AVDA outperforms previous methods in semi-supervised few-shot domain adaptation.
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.
Paper proposes DDA for better transfer learning performance.
problem Domain discrepancy between source and target distributions.
method Dynamic Distribution Adaptation (DDA) to evaluate and adapt distribution importance.
result DDA improves transfer learning performance on various tasks.
Proposes a method to predict RUL with domain adaptation for PHM.
problem Domain shift reduces predictive model performance in PHM.
method Long Short-Term Neural Networks (LSTM) for domain adaptation.
result Proposed method provides more reliable RUL predictions under different conditions.
Deep architecture learns transferable features for robust speech emotion recognition.
problem Robust and discriminative features for diverse speech emotion domains.
method Jointly uses CNN for domain-shared features and LSTM for domain-specific emotion classification.
result Transferable features provide gains up to 18.4% in speech emotion recognition.
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…
Method aggregates models with different hyper-parameters to adapt to target domain.
problem Choosing hyper-parameters for unsupervised domain adaptation.
method Linear aggregation of models with different hyper-parameters using weighted least squares for vector-valued functions.
result The target error is asymptotically not worse than twice the error of the optimal aggregation.
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.
This work provides a strong baseline for the problem of multi-source multi-target domain adaptation and generalization in medical imaging. Using a diverse collection of ten chest X-ray datasets, we empirically demonstrate the benefits of training medical imaging deep learning models on varied patient populations for ge…
New benchmark and COAL model tackle class-imbalanced domain adaptation.
problem Aligning feature and label distributions across domains with different label distributions.
method COAL model combining feature and label distribution alignment.
result COAL model outperforms recent domain adaptation methods.