Proposes MGPLL for PL learning with non-random noise.
problem Partial label learning with non-random label noise.
method Bi-directional mapping framework, conditional noise label generation, multi-class predictor, adversarial learning.
result Demonstrates state-of-the-art performance in partial label learning.
Proposes ML-GCN for multi-label network node representation learning.
problem Complex multi-label networks with correlated labels.
method Two Siamese GCNs model node-label and label-label interactions, integrated under a unified objective function.
result Effective node representation learning with preserved label interactions.
A new method learns label correlations for better multi-label predictions.
problem Label correlations not accurately characterized by existing approaches.
method Sparse reconstruction in the label space to learn correlations, then integrate into model training.
result Our approach outperforms state-of-the-art multi-label learning methods.
Paper bridges ordinary-label and complementary-label learning frameworks.
problem Combining complementary-label learning with ordinary-label learning.
method Integrates loss functions for one-versus-all and pairwise classification.
result Derives classification risk and error bound for additivity and duality loss functions.
Paper tackles multi-label zero-shot learning, improving label embedding projection for unseen classes.
problem Challenges in transferring knowledge from seen to unseen classes in multi-label zero-shot learning.
method Proposes a transfer-aware embedding projection approach to project label embeddings into a low-dimensional space for better inter-label relationships and explicit information transfer.
result Demonstrates the efficacy of the proposed approach through experiments on zero-shot multi-label image classification.
An active learner is given a hypothesis class, a large set of unlabeled examples and the ability to interactively query labels to an oracle of a subset of these examples; the goal of the learner is to learn a hypothesis in the class that fits the data well by making as few label queries as possible. This work addresses…
PML-GAN tackles noisy multi-label annotations using adversarial learning.
problem Learning multi-label models from noisy, overcomplete annotations.
method PML-GAN uses a disambiguation network and a generative adversarial network to map noisy labels to clean labels and data samples.
result PML-GAN achieves state-of-the-art performance on partial multi-label learning datasets.
Partial label learning (PLL) aims to solve the problem where each training instance is associated with a set of candidate labels, one of which is the correct label. Most PLL algorithms try to disambiguate the candidate label set, by either simply treating each candidate label equally or iteratively identifying the true…
Paper proposes a method to recover accurate labels from partially valid data in multi-label learning.
problem Tackles noisy supervision in multi-label learning with partially valid labels.
method Develops a two-stage method that estimates label enrichment and ground-truth confidences.
result Demonstrates improved performance over state-of-the-art PML methods.
An important problem in multi-label classification is to capture label patterns or underlying structures that have an impact on such patterns. This paper addresses one such problem, namely how to exploit hierarchical structures over labels. We present a novel method to learn vector representations of a label space give…
A new Q&A labeling method for assigning labels in machine learning.
problem Assigning labels to instances in supervised machine learning.
method Developed a Q&A labeling method involving a question generator and an annotator.
result The derived label generative model is consistent with previous studies.
A framework learns dynamic soft labels to improve model generalization and accuracy.
problem Models trained on one-hot labels overfit and are sensitive to noisy annotations.
method Proposes a framework where labels are treated as learnable parameters, adapting dynamically during optimization.
result Consistent gains across different datasets and architectures, improving ResNet18 by 2.1% on CIFAR100.
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.
Proposes MLPSVM for multi-label learning, improving on binary relevance.
problem Handles multi-label learning tasks more efficiently than binary relevance.
method Uses standard support vector machines with parallel decision hyper-planes.
result Outperforms other multi-label learning algorithms on various data sets.
Proposes a data augmentation method to improve multi-label learning performance.
problem Improving multi-label learning by exploiting label correlations and data augmentation.
method Proposes a novel data augmentation approach that performs clustering on real examples and treats cluster centers as virtual examples, promoting local smoothness through a regularization term.
result Extensive experiments show that the proposed method outperforms state-of-the-art multi-label learning approaches.
DM2L tackles missing labels in multi-label learning by modeling local and global rank structures.
problem Missing labels in multi-label learning.
method DM2L imposes local low-rank structures and global high-rank structures on predictions of instances from the same and different labels, respectively.
result DM2L outperforms state-of-the-art methods in multi-label learning with missing labels.
Enhances labels from unlabeled data using sample correlations.
problem Lack of label distributions in real-world applications.
method Proposes LESC and gLESC methods to enhance label distributions.
result Improves performance of label enhancement through sample correlations.
Proposes a self-paced multi-label learning method to handle diverse labels efficiently.
problem Learning from multi-label data with a large label space is NP-hard and prone to overfitting.
method Self-paced multi-label learning with diversity (SPMLD) approach, incorporating gradual label inclusion and diversity maintenance.
result The proposed SPMLD framework optimizes a non-convex objective function using block coordinate descent.
DAL uses disentanglement for automatic labeling in GAN-based active learning.
problem Reducing human labeling in GAN-based active learning.
method DAL leverages disentanglement in InfoGAN to automatically label datapoints, deciding human labeling based on disagreement with InfoGAN labels and label correction.
result DAL achieves better performance than existing GAN-based active learning approaches on image classification tasks.
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.
New method for semi-supervised learning in federated learning with and without labels at clients.
problem Training federated learning models with partially or completely unlabeled data.
method Federated Matching (FedMatch) with inter-client consistency loss and disjoint learning.
result FedMatch outperforms local semi-supervised learning and naive federated learning combinations.
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.
Curriculum Labeling improves semi-supervised learning with pseudo-labeling, achieving high accuracy with minimal labeled data.
problem Improving semi-supervised learning with limited labeled data.
method Applying curriculum learning principles and restarting model parameters before each self-training cycle.
result 94.91% accuracy on CIFAR-10 with only 4,000 labeled samples.
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.
Weak labels can significantly speed up learning for strong tasks.
problem Learning with limited strong labels.
method Using weak labels to accelerate learning of strong tasks.
result Weak labels can accelerate learning to O(icefrac1n) rate. Paper tackles label insufficiency and inaccuracy in semi-supervised learning.
problem Label insufficiency and inaccuracy in semi-supervised learning.
method Graph-based propagation for label insufficiency and label filtering for inaccuracy.
result SIIS improves performance in the presence of label noise and scarcity.
Proposes methods to improve multi-label learning by addressing local label imbalance.
problem Local label imbalance within minority class examples degrades multi-label learning performance.
method Introduces a measure to assess local label imbalance and two sampling approaches (MLSOL, MLUL) to address it.
result Experimental results show MLSOL and MLUL improve performance on multi-label datasets.
Method trains classifiers without labels using adversarial constraints.
problem Training classifiers without labeled data.
method Adversarial label learning method that trains classifiers to perform well against an adversary choosing labels.
result Method outperforms other weakly supervised learning approaches on real datasets.
It is challenging to handle a large volume of labels in multi-label learning. However, existing approaches explicitly or implicitly assume that all the labels in the learning process are given, which could be easily violated in changing environments. In this paper, we define and study streaming label learning (SLL), i.…
A new framework CL embeds features and labels for multi-label classification.
problem Exponential growth of output space in multi-label classification.
method Compact Learning (CL) framework that embeds features and labels simultaneously.
result CMLL maximizes label-feature dependency and minimizes label space loss.
This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems. We suggest that for each individual label, it cannot only be implicitly connected with other labels via the low-rank constraint over label predictors, but also its performa…
New framework for learning with class-conditional multi-label noise.
problem Class labels corrupted with conditional probabilities for multiple labels.
method Formalized as CCMN framework, established unbiased estimators, proved consistency with multi-label loss functions, implemented partial multi-label learning method.
result Effectiveness validated on multiple datasets and metrics.
Paper tackles multi-label learning by improving SVR for positive semidefinite metrics.
problem Learning positive semidefinite metrics for multi-label and label distribution learning.
method Proposes two methods to overcome SVR's limitation in learning positive semidefinite metrics.
result Demonstrates new methods achieve favorable performance in multi-label and label distribution learning.
Paper tackles interpretability in deep learning for multi-label learning.
problem Machine learning models are often hard to interpret.
method Combines deep autoencoder and multi-label classifiers.
result Proposes interpretable label hierarchies and dependencies.
A new method improves node classification in graphs with limited labels.
problem Semi-supervised multi-label node classification in attributed graphs.
method Collaborative Graph Walk (Multi-Label-Graph-Walk) using reinforcement learning.
result Significantly better multi-label classification performance compared to state-of-the-art methods.
Crowdsourcing has become very popular among the machine learning community as a way to obtain labels that allow a ground truth to be estimated for a given dataset. In most of the approaches that use crowdsourced labels, annotators are asked to provide, for each presented instance, a single class label. Such a request c…
Paper tackles online adaptation to changing label distributions.
problem Adapting machine learning models to changing label distributions in real-world settings.
method Leverages novel analysis to show estimation of expected test loss is possible without true labels. Proposes adaptation algorithms inspired by classical online learning techniques.
result Empirically verified that OGD is particularly effective and robust to various label shift scenarios.
Paper tackles representation learning from inconsistent crowdsourced labels.
problem Limited and inconsistent crowdsourced labels hinder representation learning.
method Proposes RLL framework to learn representation from limited crowdsourced labels.
result RLL outperforms state-of-the-art baselines in learning from limited labeled data.
PML-LFC improves PML by estimating label confidence from both feature and label spaces.
problem PML challenges in real-world scenarios where only some labels are relevant.
method PML-LFC estimates label confidence using feature and label space similarities, training a predictor with these values.
result PML-LFC achieves superior performance on synthetic and real-world datasets.
End-to-end deep metric learning tackles multi-label image classification.
problem Multi-label image classification problem.
method Two-way deep distance metric learning in a latent space with a reconstruction module.
result Our method outperforms state-of-the-arts on publicly available image datasets.
Semi-supervised learning improves with partial label information.
problem Improving model performance with limited labeled data.
method Contrastive learning with partial label information to encourage same labels.
result Partial label information reduces test error by up to 5.5 times.
Boosting for label ranking outperforms existing methods.
problem Improving label ranking predictions using boosting techniques.
method Proposed a boosting algorithm tailored for label ranking tasks.
result Significantly outperforms existing label ranking algorithms.
SMART simplifies data labeling for machine learning.
problem Creating labeled training data for machine learning.
method Intuitive web interface, active learning, inter-rater reliability.
result Reduces the need for labeled data and improves label quality.
Collecting labeled data is costly and thus a critical bottleneck in real-world classification tasks. To mitigate this problem, we propose a novel setting, namely learning from complementary labels for multi-class classification. A complementary label specifies a class that a pattern does not belong to. Collecting compl…
Efficient algorithms learn from coarse labels instead of fine grained ones.
problem Learning from coarse labels when fine labels are unavailable.
method Formalized coarse label settings, used a reduction for SQs, and provided efficient algorithms.
result Any problem learnable from fine labels can be learned efficiently from coarse 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).
Debiased contrastive learning improves representation learning by correcting for same-label sampling.
problem Sampling negative examples from truly different labels improves performance in self-supervised representation learning.
method Developed a debiased contrastive objective that corrects for the sampling of same-label datapoints without true labels.
result The proposed debiased contrastive objective consistently outperforms state-of-the-art methods across vision, language, and reinforcement learning benchmarks.
Novel framework for efficient entity alignment labeling.
problem Efficient labeling of entity alignments in knowledge graphs.
method Active and passive learning strategies to select informative instances.
result Passive learning achieves comparable performance to active learning.