Paper proposes a method to estimate class prior from positive and unlabeled data.
problem Estimating class prior in unlabeled datasets when labeled data is not available.
method Use penalized divergences to fit a mixture of class-wise distributions to the unlabeled data distribution.
result Correct estimation of class prior using only positive samples and penalized L1-distance. Generative model creates synthetic unlabeled data for SSL.
problem Training SSL models without real unlabeled datasets.
method Meta-optimized synthetic samples generated from generative models.
result Synthetic samples improve SSL performance more efficiently than real unlabeled data.
MTL method uses unlabeled data with pseudo labels to improve classification with disjoint datasets.
problem Improving classification performance with disjoint labeled datasets using unlabeled data.
method Proposes MTL-SA method to select and augment unlabeled data with confident pseudo labels and close distribution to labeled data.
result Extensive experiments show the effectiveness of MTL-SA method in improving classification performance.
Distilled teacher model transfers knowledge to student model on new datasets.
problem Improving model quality using unlabeled data.
method Knowledge distillation with an unlabeled teacher model.
result Teacher model knowledge transfers to student model on out-of-distribution datasets.
MixMOOD improves SSDL by selecting unlabelled data based on deep feature similarity.
problem Class distribution mismatch in semi-supervised learning.
method MixMOOD uses deep dataset dissimilarity measures to select unlabelled data.
result MixMOOD selects unlabelled data based on strong correlation with MixMatch accuracy.
Proposes PUUPL for PUL in imbalanced datasets, boosting minority class signals.
problem Imbalanced datasets and model calibration in PUL.
method Uncertainty-aware pseudo-labeling procedure (PUUPL).
result Substantial performance gains in highly imbalanced settings.
Mitigates overfitting in UU classification from two unlabeled datasets.
problem Overfitting in the UU classification method.
method Wrapping negative empirical risk terms with correction functions and proving consistency.
result Successfully mitigates overfitting and improves classification accuracy.
This paper uses unlabeled data to improve compressed neural networks.
problem Difficulty in retraining pre-trained models due to limited labeled data.
method Uses unlabeled data to mimic classification characteristics and aligns feature distributions using adversarial loss.
result Unlabeled data significantly improves the performance of compressed neural networks.
StyleDiff compares unlabeled datasets using disentangled image spaces.
problem Mismatches between development and real-world datasets lead to inaccurate predictions.
method Uses disentangled image spaces and focuses on attributes to compare datasets.
result Accurately detects and presents differences between datasets.
Deep RL detects anomalies from few labeled examples and large unlabeled data.
problem Anomaly detection with limited labeled data and large unlabeled data.
method Deep reinforcement learning to optimize detection of labeled and unlabeled anomalies.
result Significantly outperforms state-of-the-art methods on 48 real-world datasets.
New method improves adversarial learning with unlabeled data.
problem Poor quality of pseudo labels on unlabeled data.
method Robust Co-training (RCT) using deep co-training.
result RCT significantly outperforms baselines in adversarial robustness.
New methods learn from PU data with non-representative positives.
problem Learning from PU data with non-representative positive classes.
method Integrates negative-unlabeled and unlabeled-unlabeled learning, or uses a recursive risk estimator.
result Effective across various real-world datasets and forms of positive bias.
Prevents sensitive data generation in diffusion models using labeled and unlabeled data.
problem Generating sensitive data in diffusion models using unlabeled data.
method Positive-Unlabeled Diffusion Models, approximating ELBO with labeled and unlabeled data.
result Prevents the generation of sensitive data without compromising image quality.
Paper tackles leveraging unlabeled data for PU classification and robust generation.
problem Scarcity of labeled data in machine learning problems.
method Introduces a novel training framework that simultaneously targets PU classification and conditional generation using extra unlabeled data.
result Proves the effectiveness of a Classifier-Noise-Invariant Conditional GAN (CNI-CGAN) that enhances PU classifier performance and leverages extra data.
MixMatch combines unlabeled data with labeled data to improve semi-supervised learning.
problem Improving semi-supervised learning with limited labeled data.
method MixMatch uses MixUp to blend labeled and unlabeled data, guessing low-entropy labels for augmented examples.
result MixMatch achieves state-of-the-art results across various datasets and labeled data amounts.
Improves anomaly detection with contaminated unlabeled data.
problem Weakness in existing semi-supervised anomaly detection methods when unlabeled data contain anomalies.
method Integrates positive-unlabeled learning with deep anomaly detection models.
result Achieves better detection performance on various datasets.
Paper proposes a reinforcement learning approach to learn classifiers from positive and unlabeled data.
problem Learning binary classifiers from positive and unlabeled data without prior class prior or label noise estimation.
method Alternately trains a policy network and a classifier using reinforcement learning, adapting label assumptions for unlabeled data.
result Significant improvement in classification performance compared to existing methods.
Enhances U-statistics for semi-supervised datasets using unlabeled data.
problem Efficiently utilizing unlabeled data in semi-supervised settings.
method Semi-supervised U-statistics enhanced by unlabeled data.
result Proposed method is asymptotically Normal and more efficient than classical U-statistics.
Improved generative models learn from unlabeled data with semi-supervised learning.
problem Improving generative accuracy from small labeled datasets.
method Developed a parameter-efficient deep semi-supervised generative model.
result Improved performance in disentangling latent variables and prediction.
Unsupervised training improves adversarial robustness.
problem Training robust models requires large labeled datasets, which are expensive.
method Used unlabeled data to train adversarially robust models.
result Unsupervised Adversarial Training improves robust accuracy by 21.7%.
Paper improves sound event detection using semi-supervised learning.
problem Weakly labeled sound event detection in polyphonic audio clips.
method Combines tri-training and adversarial learning for semi-supervised learning.
result Significant performance improvement over baseline model.
Transfer learning helps predict social roles in unlabelled networks.
problem Recognizing social roles in unlabelled social networks.
method Transfer learning with feature transformations.
result Improved role classification accuracy on real-world datasets.
Paper proposes a method to extract style features from unlabeled data.
problem Extracting fine-grained features like styles from unlabeled data.
method Contrastive conditioned variational autoencoders with mutual information constraints.
result The method efficiently extracts style features from real-world natural image datasets.
Proposes a model for semi-supervised learning using both labeled and unlabeled data.
problem Semi-supervised learning with limited labeled data.
method Semi-conditional normalizing flow model with conditional coupling layer.
result Model outperforms variational auto-encoders on MNIST dataset.
Bayesian framework uses unlabeled data to improve fairness assessment.
problem Reliable fairness assessment with limited labeled data.
method Hierarchical latent variable model with Bayesian inference.
result Significant reduction in estimation error for fairness metrics.
BetaDataWeighter learns weights for unlabelled data to improve self-supervised learning accuracy.
problem Improving unsupervised representations with domain shift between unlabelled and target data.
method Learning Bayesian instance weights for unlabelled data to prioritize useful instances.
result BetaDataWeighter achieves highest average accuracy and prunes up to 78% of images without significant loss in accuracy.
Proposes a method to classify binary data from multiple unlabeled datasets.
problem High annotation costs in training classifiers from weakly supervised data.
method Introduces surrogate set classification (SSC) to predict data origin from multiple unlabeled datasets, then uses this to train a binary classifier.
result Demonstrates superior performance compared to existing methods.
Split-BN improves SSL by separating BN stats for unlabeled data.
problem SSL benefits from unlabeled data are often lost or even harmful when class mismatch exists.
method Split-BN uses separate BN statistics for unlabeled data.
result Split-BN improves SSL performance in shifted domain settings.
Surveying how to use unlabeled data in federated learning.
problem Costly labeling of data limits FL applications.
method Survey and analyze existing research.
result Potential for using unlabeled data in FL.
Neural approach for Chinese word segmentation using lexicon and unlabeled data.
problem Efficiently segment Chinese words with limited labeled data.
method Posterior regularization algorithm with indirect supervision from lexicon and unlabeled data.
result Validation of approach on multiple benchmark datasets in in-domain and cross-domain scenarios.
WILDS 2.0 expands benchmark datasets for unsupervised adaptation.
problem Leveraging unlabeled data for distribution shifts in real-world applications.
method Curated unlabeled data across various applications, tasks, and modalities.
result State-of-the-art methods perform poorly on WILDS datasets.
BELA infers labels for unlabeled data at lower cost.
problem Efficiently labeling large unlabeled datasets.
method Supervised splitting with bias-reduction techniques.
result BELA outperforms existing adaptive labeling strategies.
Research improves open-set learning by leveraging unlabelled data.
problem Learning between observed and unobserved novel categories.
method Unified policy of positive and unlabelled learning, semi-supervised learning, and open-set recognition.
result Achieves state-of-the-art results in open-set learning.
Paper shows using unlabeled mismatched images can improve KD for image classification.
problem Improving Knowledge Distillation for image classification with limited labeled data.
method Used unlabeled mismatched images as stimulus for KD, focusing on stimulus complexity.
result Stimulus complexity is crucial for KD's effectiveness, as demonstrated on MNIST and CIFAR datasets.
Enhances few-shot image classification using unlabelled examples.
problem Few-shot image classification with limited labeled data.
method Transductive meta-learning combining soft k-means clustering and neural feature extractor.
result State-of-the-art performance on Meta-Dataset, mini-ImageNet, and tiered-ImageNet benchmarks.
Enhances adversarial robustness with unlabeled out-of-domain data.
problem Improving robustness of models against adversarial attacks.
method Leveraging unlabeled data from multiple domains to bridge the sample complexity gap in adversarial robustness.
result Better adversarial robustness achieved when unlabeled data comes from a shifted domain.
TAP transfers knowledge from unlabeled data to improve cross-modal learning.
problem Improving supervised learning performance using unlabeled data from a different modality.
method Probabilistic approach for missing information estimation, kernel regression, cross-attention module, TAP neural network.
result TAP significantly improves generalization across different domains and neural network architectures.
In-context learning solves PU classification without iterative optimization.
problem Binary classification with only labeled positives and unlabeled samples.
method Pretrained transformer (PUICL) that learns from synthetic PU datasets.
result Outperforms four standard PU learning baselines on 20 benchmarks.
DoubleMatch combines pseudo-labeling with self-supervision for SSL.
problem Lack of effective use of unlabeled data in SSL.
method Combines pseudo-labeling with self-supervised loss.
result Achieves state-of-the-art accuracies on multiple datasets.
Discriminative clustering learns from both labeled and unlabeled data.
problem Clustering complex datasets with limited labeled data.
method Gradient-based stochastic training and optimal transport with entropic regularization.
result The method can learn feature representations even in fully unsupervised settings.
ATC predicts target domain accuracy using only labeled and unlabeled data.
problem Predicting out-of-distribution performance with limited labeled data.
method Average Thresholded Confidence (ATC) method that learns a threshold on model confidence.
result ATC outperforms previous methods across various types of distribution shifts and datasets.
ADS filters data points for efficient batch active learning.
problem Efficiently selecting data points for annotation in parallel settings.
method Active Data Shapley (ADS) using the Shapley value of data.
result Significantly increases efficiency of active learning by 6x.
Semi-supervised learning uses unlabeled data to improve fairness without sacrificing accuracy.
problem Discrimination in machine learning models.
method Pseudo labeling, re-sampling, ensemble learning.
result Unlabeled data can help achieve a better balance between accuracy and fairness.
New method uses unlabeled data to prevent forgetting in deep learning.
problem Catastrophic forgetting in lifelong learning with deep neural networks.
method Class-incremental learning scheme with global distillation, confidence-based sampling, and learning strategy.
result Significantly higher accuracy and less forgetting compared to state-of-the-art methods.
SelectNet selectively adds unlabelled data to balance imbalanced training data.
problem Training from imbalanced data in machine learning.
method SelectNet trains a deep neural network to add unlabelled data with predicted labels to the training dataset.
result SelectNet effectively balances imbalanced training data, improving classifier performance.
Efficient PU learning algorithm for positive and unlabeled data.
problem Learning binary classifier from only positive and unlabeled data.
method Proposes a computationally efficient PU learning algorithm for large-scale datasets.
result Established upper bounds of estimation error and excess risk.
ORDisCo learns from unlabeled data to improve semi-supervised continual learning.
problem Lack of effective use of unlabeled data in semi-supervised continual learning.
method Deep Online Replay with Discriminator Consistency (ORDisCo) that continually passes the learned data distribution to a classifier and selectively stabilizes discriminator parameters.
result Significant performance improvement on various semi-supervised learning benchmark datasets.
New framework assesses value of labeled vs unlabeled data in latent variable models.
problem Determining the optimal use of labeled and unlabeled data in latent variable models.
method Developed a bias-variance decomposition of the generalization error for method-of-moments latent variable estimation, and introduced a correction for misspecification.
result Labeled data is more valuable than unlabeled data when models are misspecified, but this value can be reduced with correction.