Unsupervised Domain Adaptation (DA) is used to automatize the task of labeling data: an unlabeled dataset (target) is annotated using a labeled dataset (source) from a related domain. We cast domain adaptation as the problem of finding stable labels for target examples. A new definition of label stability is proposed, …
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48 results for “target labels”
Proposes a method to adapt labels on graphs with few labeled nodes.
problem Domain adaptation for graphs with limited labeled nodes.
method Optimization problem solving label transfer using spectral graph wavelets.
result Method yields satisfactory classification accuracy compared to existing methods.
EnMDAP aligns conditional distributions for multi-source domain adaptation using pseudolabels.
problem Training a target model with no labeled data in the absence of target data labels.
method EnMDAP uses label-wise moment matching and ensemble learning with multiple feature extractors.
result EnMDAP achieves state-of-the-art performance in multi-source domain adaptation tasks.
RLLS corrects label shifts between source and target domains.
problem Correcting label shifts between source and target domains.
method Estimate importance weights using labeled source data and unlabeled target data; train classifier on weighted source samples; derive generalization bound.
result Improves classification accuracy, especially in low sample and large-shift regimes.
New methods detect targets from imprecisely labeled hyperspectral data.
problem Challenges in acquiring labeled hyperspectral data.
method Multi-Target MI-ACE and MI-SMF methods that learn target signatures from imprecisely labeled samples.
result Effective at learning target signatures and performing target detection.
We present new minimax results that concisely capture the relative benefits of source and target labeled data, under covariate-shift. Namely, we show that the benefits of target labels are controlled by a transfer-exponent that encodes how singular Q is locally w.r.t. P, and interestingly allows situations where tr…
Clarinet uses complementary labels to train classifiers with less source data.
problem Training classifiers with true-label data from source domain is costly.
method Proposes CLARINET to train classifiers with complementary-label source data and unlabeled target data.
result CLARINET significantly outperforms baselines in unsupervised domain adaptation.
New method uses limited labeled data and multiple starts to adapt models across domains.
problem Accurate predictions in target domain with few labeled data.
method Fine-tuning from multiple adaptive starts, extending UDA methods.
result Minimax-optimal target performance with limited labeled target data.
PAS method improves UDA by progressively refining subspaces for reliable pseudo-labels.
problem Mitigating mode collapse in unsupervised domain adaptation.
method Progressive Adaptation of Subspaces (PAS) approach.
result PAS effectively mitigates mode collapse and improves performance in UDA and PDA.
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.
Accelerates DNN robustness verification with target labels.
problem Improving the robustness of deep neural networks against adversarial attacks.
method Guiding robustness verification with target labels, reducing search space and using symbolic interval propagation and linear relaxation.
result Significantly improves DNN verification speed by 36X, especially when perturbation distance is reasonable.
Unsupervised models can provide supplementary soft constraints to help classify new target data under the assumption that similar objects in the target set are more likely to share the same class label. Such models can also help detect possible differences between training and target distributions, which is useful in a…
Transfer learning aims to improve learning in target domain by borrowing knowledge from a related but different source domain. To reduce the distribution shift between source and target domains, recent methods have focused on exploring invariant representations that have similar distributions across domains. However, w…
Method transfers knowledge without label overlap, source data, or target architecture consistency.
problem Difficulties in transfer learning due to label mismatch, restricted source data, and specialized target architectures.
method Uses deep generative models in two stages: pseudo pre-training and pseudo semi-supervised learning.
result Outperforms scratch training and knowledge distillation methods.
TASFAR adapts regression models without labeled source data.
problem Lack of labeled source data for domain adaptation.
method Uses prediction confidence to estimate target label distribution and calibrate source model.
result Substantially reduces errors in various regression tasks.
According to Cobanoglu et al and Murphy, it is now widely acknowledged that the single target paradigm (one protein or target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable. More often than not, a drug-like compound (ligand) can be promiscuous - that is, i…
New framework optimizes label shift adaptation using aligned distribution mixture.
problem Label shift where source and target label distributions differ.
method Aligned Distribution Mixture (ADM) framework, incorporating insights from generalization theory.
result The ADM framework improves four typical label shift methods and introduces a one-step approach.
This work explores how neural network architecture affects robustness to noisy labels.
problem The impact of neural network architecture on robustness to noisy labels.
method Formal framework connecting robustness to architecture alignments, measured by predictive power in representations.
result Network robustness to noisy labels improves when its architecture is more aligned with the target function.
Improves domain adaptation by aligning source and target distributions and mitigating noisy labels.
problem Improving performance on target images with different acquisition conditions.
method Combines optimal transport, MixUp regularization, and robust loss for noisy labels.
result Improves domain adaptation performance on various benchmarks and real-world problems.
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 presents a method to train NER models without labelled data using weak supervision.
problem Dealing with NER performance drop in new domains without labelled data.
method Weak supervision through automatic annotation and hidden Markov model integration.
result Improvement of about 7 percentage points in entity-level scores.
New method reduces label and data shifts between domains using optimal transport.
problem Label shift between source and target domains in domain adaptation.
method Developed theory and LDROT method to mitigate label and data shifts.
result Theoretical and experimental validation of LDROT's effectiveness.
Enhances neural network robustness with Mixup and TLAT.
problem Neural networks are sensitive to various perturbations and adversarial examples.
method Combines Mixup augmentation with Targeted Labeling Adversarial Training (TLAT).
result M-TLAT increases robustness against 19 corruptions and 5 adversarial attacks without reducing clean sample accuracy.
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.
New PL method improves ASR accuracy without pseudo-labels.
problem Improving ASR accuracy with limited labeled data.
method End-to-end continuous pseudo-labeling with soft-labels.
result Soft-labels can lead to model collapse, but regularization can mitigate this.
Sourcerer uses deep learning to map land cover from limited labeled data.
problem Producing accurate land cover maps with scarce labeled data.
method Bayesian-inspired, deep learning approach with a novel regularizer.
result Sourcerer outperforms other methods, even with minimal labeled target data.
Framework for domain adaptation using pseudo-labels from unlabeled data.
problem Improving prediction accuracy in target domain with covariate shift.
method Kernel GLMs with labeled and pseudo-labeled data, using imputation model for target data.
result Non-asymptotic excess-risk bounds for effective labeled sample size.
Study reveals differences in label shift problem difficulty in supervised vs. unsupervised settings.
problem Label shift problem in non-parametric classification.
method Analysis of minimax rates in supervised and unsupervised settings, focusing on class conditional distributions estimation.
result A class proportion estimation approach is minimax rate-optimal in the unsupervised setting.
Estimates calibration error under label shift without labels.
problem Ensuring model reliability in the face of dataset shift without access to labels.
method Importance re-weighting of the labeled source distribution to estimate calibration error under label shift.
result Effective and reliable CE estimation with respect to the shifted target distribution.
This paper tackles target-dependent label complexity gap in active learning.
problem Target-dependent label complexity gap in Agnostic Active Learning.
method Introduces a novel distribution-splitting strategy based on number density to reduce label complexity and error rate.
result Provides theoretical guarantees and practical advantages for reducing label complexity and error rate.
New algorithm predicts multiple types of outputs with dependencies.
problem Predicting multiple diverse types of outputs.
method Problem transformation method combined with component-wise boosting.
result Sparse and interpretable learning of dependencies between targets.
This work analyzes two methods for combining multiple binary labels in bipartite ranking.
problem Combining multiple binary labels for optimal bipartite ranking.
method Loss aggregation vs. label aggregation approaches.
result Label aggregation is preferable to loss aggregation due to label dictatorship issues.
Paper proposes a method to adapt classifiers using complementary labels instead of true labels.
problem Training classifiers with true labels from the source domain is costly and sometimes impossible.
method Proposes a novel setting with complementary labels and a complementary label adversarial network (CLARINET).
result CLARINET significantly outperforms baselines on handwritten digits and object recognition tasks.
Study shows algorithms benefit from limited target data with many source domains.
problem Adapting to new domains with scarce labeled target data.
method New family of model selection algorithms.
result Beneficial guarantees in scenarios with limited target data.
Method addresses label shift in adversarial domain adaptation.
problem Label shift in behavioral studies.
method DATS (Domain Adversarial nets for Target Shift) framework.
result DATS framework performs well under large label shift.
Transfer learning aims to faciliate learning tasks in a label-scarce target domain by leveraging knowledge from a related source domain with plenty of labeled data. Often times we may have multiple domains with little or no labeled data as targets waiting to be solved. Most existing efforts tackle target domains separa…
Domain Adaptation on Graphs by Learning Graph Topologies: Theoretical Analysis and an Algorithmstat.ML
Algorithm learns graph topologies to adapt labels between source and target graphs.
problem Domain adaptation on graphs with different data manifolds.
method Proposes a graph domain adaptation algorithm that learns both graph topologies and label functions.
result Improves classification performance as graph topologies become more balanced.
New framework for learning from various weak supervision types.
problem Scarcity of labeled data in real-world problems.
method Probabilistic framework based on maximum likelihood principle for deep neural networks.
result General method for learning from noisy labels, complementary labels, and coarse-grained labels.
The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change …
Machine learning models are vulnerable to adversarial examples. An adversary modifies the input data such that humans still assign the same label, however, machine learning models misclassify it. Previous approaches in the literature demonstrated that adversarial examples can even be generated for the remotely hosted m…
The quantification problem consists of determining the prevalence of a given label in a target population. However, one often has access to the labels in a sample from the training population but not in the target population. A common assumption in this situation is that of prior probability shift, that is, once the la…
In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to…
This review categorizes domain adaptation methods without target labels.
problem How to train a classifier from a source domain to generalize to a target domain.
method Sample-based, feature-based, and inference-based methods.
result Recurring ideas and conditions for cross-domain generalization error.
Paper proposes a new method to aggregate multiple sources with different label distributions.
problem Aggregating from multiple target-shifted sources with different label distributions.
method Unified framework to select relevant sources for domain adaptation with limited label, unsupervised, and label partial unsupervised scenarios.
result Empirical results significantly outperform baselines.
Develops a method for kernel ridge regression under covariate shift using pseudo-labels.
problem Learning a regression function with small mean squared error over a target distribution with labeled data from a different feature distribution.
method Split labeled data into two subsets, conduct kernel ridge regression on each, use imputation model to fill missing labels, and select the best candidate model.
result Non-asymptotic excess risk bounds demonstrate effective adaptation to target distribution and covariate shift.
Paper proposes a transfer learning method for learning with label proportions that handles uncertain data.
problem Learning with label proportions (LLP) for uncertain data.
method Transfer learning approach to transfer knowledge from source to target tasks with uncertain data.
result The proposed TL-LLP method achieves better accuracy and is less sensitive to noise.
This work evaluates PDA methods without target labels, revealing significant accuracy drops.
problem Evaluating PDA methods without target labels and inconsistent experimental settings.
method Realistic evaluation of 7 PDA methods with 7 model selection strategies on 2 datasets.
result Accuracy drops up to 30 percentage points without target labels, only one method performs well.
This paper tackles adversarial perturbations in multi-label classification problems.
problem Vulnerability and robustness of multi-label learning models under adversarial attacks.
method Proposes a general attacking framework and a ranking-based framework for generating multi-label adversarial perturbations.
result Demonstrates the effectiveness of the proposed frameworks and provides insights into the vulnerability of multi-label deep learning models.