Study how noisy labels affect semi-supervised learning.
problem Effect of noisy labels on semi-supervised learning performance.
method Proposed an algorithm derived from a continuous relaxation of the Maximum A Posteriori (MAP) estimator for a Degree Corrected Stochastic Block Model (DC-SBM).
result Our approach achieves promising performance even with very noisy labeled data.
Study corrects misaligned cadaster maps using noisy supervision.
problem Correcting misaligned cadaster maps with noisy supervision data.
method Iterative training rounds to refine ground truth annotations.
result Reduces noise in cadaster map alignment datasets.
A new method reduces noise in multi-label data and reduces dimensionality.
problem Handling noisy multi-label data in semi-supervised settings.
method Semi-supervised and multi-label dimensionality reduction method using label propagation.
result NMLSDR outperforms state-of-the-art algorithms in reducing noise and dimensionality.
Paper tackles noisy S-D data for classification.
problem Learning from noisy Similar (S) and Dissimilar (D) pairs.
method Proposes two algorithms to learn from noisy S-D data under two noise models.
result Noise-informed algorithms outperform noise-blind baselines.
Self-supervised methods learn from noisy data alone, useful for imaging problems.
problem Inferring signals from noisy and incomplete observations.
method Learning a solver from measurement data alone, without ground-truth references.
result Self-supervised methods can learn meaningful estimates from noisy data.
Study semi-supervised learning with noisy proxy covariates, deriving bounds and showing gains.
problem Learning from noisy proxy covariates with scarce labels.
method Two-stage estimator learning kernel eigenfeatures from all proxy covariates and fitting a ridge predictor on labeled data.
result Finite sample bounds show fast labeled sample rates and consistent gains over supervised and semi-supervised baselines.
Paper introduces a noisy-labeled audio tagging challenge.
problem Acoustic mismatch and noisy labels in audio tagging.
method Large dataset with minimal supervision, convolutional neural network baseline.
result Demonstrates effectiveness of minimal supervision in noisy conditions.
Self-supervised method predicts clean signal and noise distribution from noisy images.
problem Blind denoising and noise estimation in biomedical images with limited clean data.
method Two neural networks jointly predict clean signal and noise distribution from noisy observations.
result Significantly outperforms state-of-the-art algorithms on six biomedical image datasets.
Improved graph attention model for noisy graphs.
problem Understanding and improving graph attention in noisy graphs.
method Proposes SuperGAT, a self-supervised graph attention network.
result SuperGAT learns more expressive attention by encoding edges.
Proposes PA-DSL for correcting noisy human labels in automated data labeling.
problem Noisy human labels in automated data labeling.
method Uses adjudicated cases to correct noisy human labels and debias analyses.
result Maintains nominal coverage and reduces RMSE by 10-17% relative to using only adjudicated labels.
Class labels are often imperfectly observed, due to mistakes and to genuine ambiguity among classes. We propose a new semi-supervised deep generative model that explicitly models noisy labels, called the Mislabeled VAE (M-VAE). The M-VAE can perform better than existing deep generative models which do not account for l…
SELF filters noisy labels to improve deep learning performance.
problem Overfitting to noisy labels in deep learning.
method Self-ensemble label filtering (SELF) using running averages of predictions.
result SELF improves task performance by filtering noisy labels dynamically.
Consistency regularization improves robustness to noisy labels.
problem Improving model robustness to noisy labels in machine learning.
method Empirical study of consistency regularization on noisy datasets.
result Consistency regularization improves model robustness to label noise.
Improved speech enhancement using diffusion models with MSE loss.
problem Efficient incorporation of noisy speech in generative speech enhancement.
method Augmented diffusion-based generative model with a MSE loss for enhanced speech.
result Proposed method improves speech enhancement performance compared to original diffusion model.
Unified approach to learning from noisy labels using auxiliary clean labels.
problem Learning from noisy labels in real-world applications.
method Rotational-Decoupling Consistency Regularization (RDCR) framework integrating consistency-based methods and self-supervised rotation task.
result RDCR achieves comparable or superior performance than state-of-the-art methods under small noise, significantly outperforming existing methods under large noise.
The paper cleans label noise in supervised classification using Bernoulli sampling.
problem Label noise degrades supervised classifier performance.
method Proposes a label noise cleaning method based on Bernoulli random sampling.
result The method separates clean and noisy observations without prior label information.
The paper improves semi-supervised learning using f-divergences and α-Rényi divergences.
problem Improving semi-supervised learning with noisy pseudo-labels.
method Inspired by f-divergences and α-Rényi divergences, the paper develops new empirical risk functions and regularization techniques. result The new methods show better performance than traditional self-training methods, especially in noisy pseudo-label scenarios.
CNT leverages noisy targets to guide model learning.
problem Learning from noisy or incomplete labels.
method Conditioning model on noisy targets at inference time.
result Model focuses on simpler sub-problems and learns from easier examples first.
Class2Simi reduces noise in noisy label learning by transforming noisy class labels into noisy similarity labels.
problem Learning with noisy labels in supervised and unsupervised settings.
method Transforming noisy class labels into noisy similarity labels, training DNNs from noisy data pairs.
result The noise rate reduction is theoretically guaranteed, making it easier to handle noisy similarity labels.
Study on noise models for noisy labels in NLP.
problem Quality of noise models from noisy labels.
method Theoretical analysis and synthetic dataset creation.
result Expected error of noise models derived.
Self-training with noisy student-teacher boosts keyword spotting accuracy.
problem Robust keyword spotting in challenging conditions.
method Aggressive data augmentation and self-training with noisy student-teacher approach.
result Significant accuracy improvement in difficult conditions, up to 60%.
UDVD uses deep learning to denoise videos without supervision.
problem Lack of clean video data for training deep learning models.
method UDVD is a CNN trained solely on noisy video data, adapting to local motion.
result UDVD performs as well as supervised methods, even with limited training data.
Paper proposes a novel model to improve n-ary cross-sentence relation extraction by addressing noisy data and non-consecutive sentences.
problem Noisy labeled data and non-consecutive sentences in n-ary cross-sentence relation extraction.
method Two-level agent reinforcement learning model and hybrid attention mechanism/PCNN approach.
result The model reduces the impact of noisy data and achieves better performance.
Weakly supervised model segments tumor areas from whole slide images.
problem Training segmentation models on noisy labeled data from whole slide images.
method Online patch sampling and robust KL divergence extension.
result Model successfully segments tumor areas with strong morphological consistency.
Probabilistic decoupling separates labels from classes for improved classification.
problem Improving classification accuracy with noisy or partially labeled data.
method Probabilistic decoupling of labels from underlying classes.
result Method enhances performance on various classification tasks, including noisy and partially labeled data.
Method estimates noise transition matrix from noisy labels without relying on unreliable class-posterior estimation.
problem Estimating noise transition matrix from noisy data.
method Total variation regularization to encourage distinguishable predicted probabilities.
result Consistent estimator of the noise transition matrix under mild assumptions.
Peer loss functions learn from noisy labels without noise rate specification.
problem Learning from noisy labels without knowing noise rates.
method Introduced peer loss functions within ERM framework.
result Peer loss functions lead to optimal or near-optimal classifiers.
In this paper we address speaker-independent multichannel speech enhancement in unknown noisy environments. Our work is based on a well-established multichannel local Gaussian modeling framework. We propose to use a neural network for modeling the speech spectro-temporal content. The parameters of this supervised model…
JoCoR improves deep learning with noisy labels by reducing network diversity.
problem Learning with noisy labels in deep learning.
method JoCoR uses two networks to make predictions, calculates a joint loss with Co-Regularization, and updates both networks simultaneously.
result JoCoR outperforms state-of-the-art approaches in learning with noisy labels.
Expands weak supervision by allowing partial labels from multiple noisy sources.
problem Creating models without labeled data using heuristic labelers.
method Probabilistic generative model estimating partial label accuracies.
result Improved model accuracy on various tasks (8.6% on text, comparable to zero-shot methods on images).
SelectMix improves deep learning robustness against noisy labels.
problem Deep neural networks memorize noisy labels, degrading performance.
method Confidence-guided targeted sample mixing with soft labels.
result SelectMix consistently outperforms baseline methods on noisy label datasets.
A new method simplifies noisy data filtering for CNNs.
problem Training CNNs with noisy labels is challenging.
method Joint Negative and Positive Learning (JNPL) combines NL+ and PL+ loss functions.
result Significantly simplifies the pipeline, achieving state-of-the-art accuracy.
Paper introduces a noise-robust classification method using hypergraph neural networks.
problem Noisy label learning problem in image datasets.
method PCA for dimensionality reduction, then applies graph-based semi-supervised learning methods including hypergraph neural network.
result Our proposed hypergraph neural network achieves the best performance when noise level increases.
Paper proposes a robust deep graph-based classifier for noisy labels.
problem Difficulty in feature learning with noisy training labels.
method Convolutional neural networks with graph Laplacian regularization (GLR).
result Proposed method outperforms state-of-the-art classifiers on noisy datasets.
Faster weak supervision framework using triplet methods.
problem Computational inefficiency in weak supervision models.
method Closed-form solution for latent variable models, avoiding iterative methods.
result Orders of magnitude faster than previous approaches.
A new speech enhancement method using variational autoencoders.
problem Improving speech quality in noisy environments.
method Using a variational autoencoder as a speech model, trained with unsupervised noise modeling.
result The method outperforms existing techniques in speech enhancement.
Framework prevents deep learning models from memorizing noisy labels.
problem Deep learning models memorize noisy labels during early learning phase.
method Develops a technique that exploits early learning phase via regularization.
result Framework achieves robustness to noisy annotations on benchmarks and real-world datasets.
Paper proposes a new meta-learning approach for correcting noisy labels.
problem Learning with noisy labels in machine learning models.
method Meta-learned instance re-weighting approach extended to label correction problem.
result Proposed MLC (Meta Label Correction) framework achieves large improvements over previous methods.
Stoch-GALL learns from noisy labels to improve model performance.
problem Training machine learning models with limited labeled data.
method Stochastic generalized adversarial label learning framework.
result Stoch-GALL outperforms weakly supervised learning methods in noisy label settings.
New method learns from noisy data without knowing noise level.
problem Learning from noisy data without knowing noise level.
method Uses Stein's Unbiased Risk Estimate (SURE) without noise level knowledge.
result Outperforms other self-supervised methods on imaging problems.
DiffDenoise preserves fine structures in medical images using conditional diffusion models.
problem Medical image denoising often results in loss of fine structures.
method Conditional diffusion model with stabilized reverse sampling and supervised training.
result DiffDenoise outperforms state-of-the-art methods in medical image denoising.
Novel framework learns policies from noisy expert demonstrations.
problem Learning effective policies with noisy expert demonstrations.
method Adaptive learning framework that jointly interacts with the environment and expert demonstrations, assigning weights to filter out noisy demonstrations.
result The proposed approach learns robustly with noisy demonstrations and achieves higher performance in fewer iterations.
Paper tackles robust training with noisy labels using a trusted set and pseudo labels.
problem Training deep neural networks with high label noise.
method Leveraging a small trusted set to estimate exemplar weights and pseudo labels for noisy data.
result Achieves high accuracy on large-scale datasets with real-world label noise.
Proposes a new method to handle noisy labels without needing accurate noise transition estimation.
problem Learning with noisy labels in the presence of class-conditional noise.
method Introduces a Latent Class-Conditional Noise (LCCN) model that embeds noise transition in a Bayesian framework and iteratively infers latent labels.
result Demonstrates superior performance compared to state-of-the-art methods on various noisy label datasets.
Improved ImageNet classification with semi-supervised learning.
problem Image classification with limited labeled data.
method Noisy Student Training: semi-supervised learning with noisy student models.
result 88.4% top-1 accuracy on ImageNet, 2.0% better than state-of-the-art.
Method learns true labels from noisy annotators using regularization.
problem Learning from noisy labels in supervised learning.
method Regularized estimation of annotator confusion matrices.
result Method outperforms state-of-the-art methods in image classification.
This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach to speech enhancement is to train a deep neural network (DNN) to take noisy speech as input and output clean speech. Although this supervis…
Improves label propagation for weakly supervised learning.
problem Reducing the need for labeled data in machine learning.
method Label Propagation with Weak Supervision (LPA) analysis.
result Demonstrated improvements over existing methods on weakly supervised classification tasks.