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.
Study real-world noisy labels from human annotations for better understanding.
problem Understanding and modeling real-world label noise in machine learning.
method Developed two new benchmark datasets (CIFAR-10N, CIFAR-100N) with human-annotated real-world noisy labels.
result Real-world noisy labels exhibit instance-dependent patterns, not class-dependent as previously assumed.
Paper proposes an alternative to anchor points for learning with noisy labels.
problem Learning with noisy labels is challenging due to inaccurate labels.
method Estimates transition matrix using clusterability condition and noisy labels.
result Estimation of transition matrix is more accurate and efficient than anchor points.
Enhances AI models with human feedback for noisy data.
problem Improving AI model alignment with human feedback in noisy environments.
method Two-stage SL+LHF framework connecting machine learning with human feedback.
result The LNCA ratio identifies conditions for SL+LHF superiority over pure SL.
ANTIDOTE reduces noisy labels influence during learning.
problem Learning with noisy labels.
method Information-divergence neighborhood relaxation and adversarial training.
result ANTIDOTE outperforms standard cross-entropy loss in noisy label settings.
ExpertNet uses noisy labels to improve deep learning robustness.
problem Improving deep learning robustness against noisy labels.
method ExpertNet framework combining Amateur and Expert models, iteratively learning from noisy labels and images.
result ExpertNet achieves robust classification with as little as 20-50% training data, outperforming state-of-the-art models.
It is important to learn various types of classifiers given training data with noisy labels. Noisy labels, in the most popular noise model hitherto, are corrupted from ground-truth labels by an unknown noise transition matrix. Thus, by estimating this matrix, classifiers can escape from overfitting those noisy labels. …
Despite the success of deep neural networks (DNNs) in image classification tasks, the human-level performance relies on massive training data with high-quality manual annotations, which are expensive and time-consuming to collect. There exist many inexpensive data sources on the web, but they tend to contain inaccurate…
A method for collecting human supervision that combines rules and instance labels.
problem Lack of labeled data and inefficient human supervision.
method Rule-exemplar method with training algorithm for joint denoising and model training.
result Our algorithm is more accurate than existing methods and effectively denoises rules.
Proposes a graph-based method for active learning in regression.
problem Active learning for regression is understudied and faces challenges with noisy labels and parameter uncertainty.
method Formulates active regression as a bipartite graph optimization problem.
result Demonstrates the effectiveness of the proposed graph-based approach on benchmark data sets.
Relation extraction aims to extract relational facts from sentences. Previous models mainly rely on manually labeled datasets, seed instances or human-crafted patterns, and distant supervision. However, the human annotation is expensive, while human-crafted patterns suffer from semantic drift and distant supervision sa…
CORES2 removes noisy labels by sieving out corrupted examples.
problem Instance-dependent label noise degrades DNN performance.
method CORES2 (COnfidence REgularized Sample Sieve) progressively sieves out corrupted examples.
result CORES2 provides theoretical guarantees for filtering out corrupted examples.
Hölder-DPO aligns models robustly with noisy human feedback.
problem No existing alignment methods can handle severe label noise.
method Proposes Hölder-DPO, a principled alignment loss with provable redescending property.
result Hölder-DPO enables scalable human feedback valuation and improves model alignment.
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.
Image classification problems are typically addressed by first collecting examples with candidate labels, second cleaning the candidate labels manually, and third training a deep neural network on the clean examples. The manual labeling step is often the most expensive one as it requires workers to label millions of im…
Bayesian method improves segmentation accuracy with noisy labels.
problem Annotation errors in semantic segmentation due to mislabeling and spatial correlations.
method Approximate Bayesian estimation with spatially correlated discrete distributions and variational inference.
result The method achieves performance comparable to clean labels under moderate noise levels.
QActor optimizes learning from noisy labeled data streams by querying experts for clean labels.
problem Learning from noisy labeled data in continuous streams with limited oracle queries.
method Combines quality models for filtering and oracle queries for true labels, dynamically adjusting query limits.
result QActor nearly matches optimal accuracy with up to 6% additional ground truth data from experts.
The paper studies how to allocate human validation in AI-assisted tasks to minimize errors.
problem Heterogeneous reliability of AI-generated signals across tasks, products, and customer segments.
method Tuned prediction-powered inference, upper confidence bounds policy, Neyman square-root rule.
result The proposed policy outperforms uniform and epsilon-greedy allocation, closing most of the gap to the oracle when reliability is heterogeneous.
S4 learns new self-supervision automatically, improving accuracy with less human effort.
problem Lack of direct supervision in machine learning.
method Combines deep learning and probabilistic logic to automatically generate and verify new self-supervision.
result S4 can automatically propose accurate self-supervision, matching supervised methods with less human effort.
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.
CbMLC improves multi-label classification with noisy labels.
problem Evaluating multi-label classifiers with noisy labels.
method Context-Based Multi-Label Classifier (CbMLC) that handles noisy labels without additional supervision.
result CbMLC yields substantial improvements over previous methods in noisy label settings.
In this paper, we consider a new low-quality label learning problem: learning time series detection models from temporally imprecise labels. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy time stamps corresponding to the occurrence of positive class e…
Paper tackles noisy similarity labels for multi-class classification.
problem Learning multi-class classifiers from noisy similarity-labeled data.
method Proposes a method using a noise transition matrix to learn from noisy data.
result Demonstrates superior performance compared to state-of-the-art methods.
Proposes a new model for noisy labels considering multiple labelers and adversarial attacks.
problem Real-world noisy label models with multiple labelers and adversarial attacks.
method Labeler-dependent noise model with adversarial attack vectors.
result State-of-the-art approaches for learning from noisy labels are defeated by adversarial label attacks.
This research tackles image classification with noise, proposing committees of CNNs.
problem Image classification with concurrent feature and label noise.
method Committees of Convolutional Neural Networks (CNNs) for MNIST, CIFAR-10, and CIFAR-100 datasets.
result Committees outperform single models in noisy conditions, especially on difficult datasets.
Deep neural networks (DNNs) are powerful tools in computer vision tasks. However, in many realistic scenarios label noise is prevalent in the training images, and overfitting to these noisy labels can significantly harm the generalization performance of DNNs. We propose a novel technique to identify data with noisy lab…
Paper explains why small-loss criterion works for learning from noisy labels.
problem Learning from noisy labels in deep learning with limited labeled data.
method Theoretical analysis and reformulation of the small-loss criterion.
result Theoretical explanation and reformulation of the small-loss criterion.
Study reveals pervasive label errors in test sets, affecting machine learning benchmarks.
problem Label errors in test sets destabilize machine learning benchmarks.
method Identified label errors in 10 common datasets using confident learning algorithms and human validation.
result Lower capacity models may be more useful in real-world datasets with high proportions of erroneously labeled data.
Deep neural networks (DNNs) trained on large-scale datasets have exhibited significant performance in image classification. Many large-scale datasets are collected from websites, however they tend to contain inaccurate labels that are termed as noisy labels. Training on such noisy labeled datasets causes performance de…
DynaCor detects noisy labels by learning from corrupted training signals.
problem Label noise in real-world datasets hinders model generalization.
method DynaCor introduces label corruption to indirectly simulate noisy labels and learns to distinguish clean from noisy instances.
result DynaCor outperforms state-of-the-art competitors in noisy label detection.
Proposes coreset method for robust training of neural networks with noisy labels.
problem Overfitting of neural networks trained with noisy labels.
method Selects weighted subsets (coresets) of clean data points to approximate low-rank Jacobian matrix.
result Gradient descent applied to coreset subsets does not overfit noisy labels.
Noisy labeled data represent a rich source of information that often are easily accessible and cheap to obtain, but label noise might also have many negative consequences if not accounted for. How to fully utilize noisy labels has been studied extensively within the framework of standard supervised machine learning ove…
The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain. Traditionally, the label noises have been treated as statistical outliers, and approaches such as importance re-weighting and bootstrap have been proposed …
Human annotations serve an important role in computational models where the target constructs under study are hidden, such as dimensions of affect. This is especially relevant in machine learning, where subjective labels derived from related observable signals (e.g., audio, video, text) are needed to support model trai…
Efficiently learns from partial labels using variational inference.
problem Learning from noisy and ambiguous partial labels in crowdsourcing.
method Amortized variational inference for probabilistic posterior approximation.
result Achieves state-of-the-art performance in accuracy and efficiency.
CrossFilter tackles noisy labels in audio tagging.
problem Noisy labels in large audio datasets.
method CrossFilter framework using multiple representations and multi-task learning.
result Improves audio tagging performance on FSDKaggle2018 and FSDKaggle2019 datasets.
Recently deep neural networks have shown their capacity to memorize training data, even with noisy labels, which hurts generalization performance. To mitigate this issue, we provide a simple but effective baseline method that is robust to noisy labels, even with severe noise. Our objective involves a variance regulariz…
Survey on deep learning robust training methods for noisy labels.
problem Dealing with noisy labels in deep learning models.
method Comprehensive review of 62 robust training methods categorized by their approach.
result Analysis of noise rate estimation and evaluation methodologies.
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.
Retraining with predicted labels improves model accuracy in noisy settings.
problem Improving model accuracy with noisy or corrupted labels.
method Retraining with predicted hard labels in a linearly separable binary classification setting.
result Retraining with predicted labels can increase model accuracy, as proven theoretically.
Leveraging weak or noisy supervision for building effective machine learning models has long been an important research problem. Its importance has further increased recently due to the growing need for large-scale datasets to train deep learning models. Weak or noisy supervision could originate from multiple sources i…
Bayes classifier cannot be learned from noisy labels without knowing noise distribution.
problem Learning a Bayes classifier from noisy labels when the noise distribution is unknown.
method Demonstrates the identifiability issues and proposes a simple algorithm for learning the Bayes decision rule.
result The Bayes decision rule is generally unidentified and cannot be learned without knowing the noise distribution.
Because large, human-annotated datasets suffer from labeling errors, it is crucial to be able to train deep neural networks in the presence of label noise. While training image classification models with label noise have received much attention, training text classification models have not. In this paper, we propose an…
Paper proposes a modified uncertainty sampling method to speed up preference learning from noisy humans.
problem Learning preferences from humans with limited queries and noisy responses.
method Modified uncertainty sampling using expected output value to speed up preference learning.
result The modified method outperforms the baseline uncertainty sampling in preference learning.
A new PLL method uses class activation values to improve robustness.
problem Weakly supervised learning with noisy data and adversarial perturbations.
method Subjective logic with class activation values for uncertainty representation and label weight re-distribution.
result More robust predictions under high noise levels, out-of-distribution examples, and adversarial perturbations.
MSLG generates soft labels to improve DNN performance on noisy datasets.
problem Significant performance degradation of DNNs due to noisy labels.
method Meta-learning techniques to estimate optimal label distribution and iteratively update soft labels.
result MSLG outperforms state-of-the-art methods by a large margin on various datasets.
INN method refines clean labeled data from noisy labels.
problem Handling noisy labels in deep neural networks.
method INN method based on memorization effect at neighbor regions.
result INN method resolves memorization effect shortcomings.
Scarcity of labeled data is a bottleneck for supervised learning models. A paradigm that has evolved for dealing with this problem is data programming. An existing data programming paradigm allows human supervision to be provided as a set of discrete labeling functions (LF) that output possibly noisy labels to input in…