Papers Partial Label Learning
“Partial Label Learning” 태그가 달린 논문 91편 · 필터 해제
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning
Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label disam…
Partial Label LearningWeakly-supervised LearningMeta Objective Guided Disambiguation for Partial Label Learning
Partial label learning (PLL) is a typical weakly supervised learning framework, where each training instance is associated with a candidate label set, among which only one label is valid. To solve PLL problems, typically…
Partial Label LearningvalidWeakly-supervised LearningPLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification
Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, given the fact that it could be expensive in practice to annotate all labels…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2ProPaLL: Probabilistic Partial Label Learning
Partial label learning is a type of weakly supervised learning, where each training instance corresponds to a set of candidate labels, among which only one is true. In this paper, we introduce ProPaLL, a novel probabilis…
Partial Label LearningWeakly-supervised LearningCASS: Cross Architectural Self-Supervision for Medical Image Analysis
Recent advances in deep learning and computer vision have reduced many barriers to automated medical image analysis, allowing algorithms to process label-free images and improve performance. However, existing techniques …
ClassificationMedical Image AnalysisMedical Image ClassificationPartial Label Learning+1Progressive Purification for Instance-Dependent Partial Label Learning
Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is correct. In the last few years, the instance-…
Partial Label LearningDecompositional Generation Process for Instance-Dependent Partial Label Learning
Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume tha…
Partial Label LearningWeakly-supervised LearningCMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. Most current method…
Image ClassificationPartial Label LearningWebly-Supervised Fine-Grained Recognition with Partial Label Learning
The task of webly-supervised fne-grained recognition is to boost recognition accuracy of classifying subordinate categories (e.g., different bird species)by utilizing freely available but noisy web data.As the label nois…
Partial Label LearningPiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite t…
Contrastive LearningPartial Label LearningPICORepresentation LearningLearning with Proper Partial Labels
Partial-label learning is a kind of weakly-supervised learning with inexact labels, where for each training example, we are given a set of candidate labels instead of only one true label. Recently, various approaches on …
Partial Label LearningWeakly-supervised LearningInstance-Dependent Partial Label Learning
Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume tha…
Partial Label LearningWeakly-supervised LearningContrastive Label Disambiguation for Partial Label Learning
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite …
Contrastive LearningPartial Label LearningPICORepresentation LearningExploiting Class Activation Value for Partial-Label Learning
Partial-label learning (PLL) solves the multi-class classification problem, where each training instance is assigned a set of candidate labels that include the true label. Recent advances showed that PLL can be compatibl…
Multi-class ClassificationPartial Label LearningOn the Robustness of Average Losses for Partial-Label Learning
Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL). In PLL, identification-based strategy (IBS) purifies each PL on the fly to selec…
Partial Label LearningWeakly Supervised ClassificationLeveraged Weighted Loss for Partial Label Learning
As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of them is true. Despite many methodology stud…
Partial Label LearningWeakly-supervised LearningFew-Shot Partial-Label Learning
Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label…
Few-Shot LearningMetric LearningPartial Label LearningvalidOPAM: Online Purchasing-behavior Analysis using Machine learning
Customer purchasing behavior analysis plays a key role in developing insightful communication strategies between online vendors and their customers. To support the recent increase in online shopping trends, in this work,…
BIG-bench Machine LearningPartial Label LearningSemi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization
Partial label learning assumes inaccurate supervision where each training example is associated with a set of candidate labels, among which only one is valid. In many real-world scenarios, however, it is costly and time-…
Partial Label LearningvalidOn the Power of Deep but Naive Partial Label Learning
Partial label learning (PLL) is a class of weakly supervised learning where each training instance consists of a data and a set of candidate labels containing a unique ground truth label. To tackle this problem, a majori…
Partial Label LearningWeakly-supervised Learning