Papers Partial Label Learning
“Partial Label Learning” 태그가 달린 논문 91편 · 필터 해제
Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning
Multi-instance partial-label learning (MIPL) addresses scenarios where each training sample is represented as a multi-instance bag associated with a candidate label set containing one true label and several false positiv…
Partial Label LearningAsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning
Partial-Label Learning (PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but…
Partial Label LearningPartially Labeled DatasetsWeakly-supervised LearningOn Characterizing and Mitigating Imbalances in Multi-Instance Partial Label Learning
*Multi-Instance Partial Label Learning* (MI-PLL) is a weakly-supervised learning setting encompassing *partial label learning*, *latent structural learning*, and *neurosymbolic learning*. Unlike supervised learning, in M…
Long-tail LearningPartial Label LearningWeakly-supervised LearningPartial Label Learning with Focal Loss for Sea Ice Classification Based on Ice Charts
Sea ice, crucial to the Arctic and Earth's climate, requires consistent monitoring and high-resolution mapping. Manual sea ice mapping, however, is time-consuming and subjective, prompting the need for automated deep lea…
ClassificationPartial Label LearningSuperpixelwise Low-rank Approximation based Partial Label Learning for Hyperspectral Image Classification
Insufficient prior knowledge of a captured hyperspectral image (HSI) scene may lead the experts or the automatic labeling systems to offer incorrect labels or ambiguous labels (i.e., assigning each training sample to a g…
Hyperspectral Image Classificationimage-classificationImage ClassificationPartial Label LearningGraph Partial Label Learning with Potential Cause Discovering
Graph Neural Networks (GNNs) have garnered widespread attention for their potential to address the challenges posed by graph representation learning, which face complex graph-structured data across various domains. Howev…
Graph Representation LearningPartial Label LearningRepresentation LearningWeakly-supervised LearningOut-of-distribution Partial Label Learning
Partial Label Learning (PLL) tackles model learning from the data with inexact labels under the assumption that training and test objects are in the same distribution, i.e., closed-set scenario. Nevertheless, this assump…
object-detectionObject DetectionOut-of-Distribution DetectionOut of Distribution (OOD) Detection+2Understanding Self-Distillation and Partial Label Learning in Multi-Class Classification with Label Noise
Self-distillation (SD) is the process of training a student model using the outputs of a teacher model, with both models sharing the same architecture. Our study theoretically examines SD in multi-class classification wi…
Multi-class ClassificationPartial Label LearningPseudo-labelling meets Label Smoothing for Noisy Partial Label Learning
Partial label learning (PLL) is a weakly-supervised learning paradigm where each training instance is paired with a set of candidate labels (partial label), one of which is the true label. Noisy PLL (NPLL) relaxes this c…
Partial Label LearningPseudo LabelWeakly-supervised LearningPartial-Label Learning with a Reject Option
In real-world applications, one often encounters ambiguously labeled data, where different annotators assign conflicting class labels. Partial-label learning allows training classifiers in this weakly supervised setting,…
Autonomous DrivingPartial Label LearningAppeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning
In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth. The majority of the existing works focuses on constructing robust classifiers to estimate …
Partial Label LearningAdaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning
There has been significant attention devoted to the effectiveness of various domains, such as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the performance of methods for noisy label lea…
Contrastive LearningMeta-LearningPartial Label LearningActive Learning for Semantic Segmentation with Multi-class Label Query
This paper proposes a new active learning method for semantic segmentation. The core of our method lies in a new annotation query design. It samples informative local image regions (e.g., superpixels), and for each of su…
Active LearningMultiple Instance LearningPartial Label LearningSegmentation+2Robust Representation Learning for Unreliable Partial Label Learning
Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. However, this idealistic assumption may not…
Contrastive LearningPartial Label LearningRepresentation LearningWeakly-supervised LearningPartial Label Supervision for Agnostic Generative Noisy Label Learning
Noisy label learning has been tackled with both discriminative and generative approaches. Despite the simplicity and efficiency of discriminative methods, generative models offer a more principled way of disentangling cl…
Image GenerationLearning with noisy labelsPartial Label LearningPartial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples
Partial-label learning (PLL) relies on a key assumption that the true label of each training example must be in the candidate label set. This restrictive assumption may be violated in complex real-world scenarios, and th…
Partial Label LearningTowards Unbiased Exploration in Partial Label Learning
We consider learning a probabilistic classifier from partially-labelled supervision (inputs denoted with multiple possibilities) using standard neural architectures with a softmax as the final layer. We identify a bias p…
Partial Label LearningOn Learning Latent Models with Multi-Instance Weak Supervision
We consider a weakly supervised learning scenario where the supervision signal is generated by a transition function $\sigma$ of labels associated with multiple input instances. We formulate this problem as \emph{multi-i…
Partial Label LearningWeakly-supervised LearningPartial-Label Regression
Partial-label learning is a popular weakly supervised learning setting that allows each training example to be annotated with a set of candidate labels. Previous studies on partial-label learning only focused on the clas…
Partial Label LearningregressionWeakly-supervised LearningSuperpixelwise Low-Rank Approximation-Based Partial Label Learning for Hyperspectral Image Classification
Insufficient prior knowledge of a captured hyperspectral image (HSI) scene may lead the experts or the automatic labeling systems to offer incorrect labels or ambiguous labels (i.e., assigning each training sample to a g…
Hyperspectral Image Classificationimage-classificationImage ClassificationPartial Label Learning+1