Paper Citation Record · LEDGER

Multi-Label Contrastive Learning : A Comprehensive Study

As of 18 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2412.00101.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.00101 v2

Coverage vector

measured 49 of 49 reference resolution

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measured 50 of 50 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: arxiv_reference, observed 2026-05-10T10:49:56.009127Z

Reference resolution

49 of 49 outbound references displayed

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External citation measurements

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Outbound references

Observation 6ab46497-5a6f-46ec-847c-27f6c3abdeff · outbound

This paper cites Query2Label: A Simple Transformer Way to Multi-Label Classification.

Multi-Label Contrastive Learning : A Comprehensive Study Query2Label: A Simple Transformer Way to Multi-Label Classification

Reference 1

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This paper cites An exploration of encoder-decoder approaches to multi-label classification for legal and biomedical text.

Multi-Label Contrastive Learning : A Comprehensive Study An exploration of encoder-decoder approaches to multi-label classification for legal and biomedical text

Reference 2

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This paper cites Large-scale multi-label text classification on EU legislation.

Multi-Label Contrastive Learning : A Comprehensive Study Large-scale multi-label text classification on EU legislation

Reference 3

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This paper cites Emograph: Capturing emotion correlations using graph networks, 2020.

Multi-Label Contrastive Learning : A Comprehensive Study Emograph: Capturing emotion correlations using graph networks, 2020

Reference 4

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This paper cites Label-representative graph convolutional network for multi-label text classification.

Multi-Label Contrastive Learning : A Comprehensive Study Label-representative graph convolutional network for multi-label text classification

Reference 5

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This paper cites Multi-label image recogni- tion with graph convolutional networks.

Multi-Label Contrastive Learning : A Comprehensive Study Multi-label image recogni- tion with graph convolutional networks

Reference 6

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This paper cites SGM: sequence generation model for multi-label classification.

Multi-Label Contrastive Learning : A Comprehensive Study SGM: sequence generation model for multi-label classification

Reference 7

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This paper cites Orderless recurrent models for multi-label classification.

Multi-Label Contrastive Learning : A Comprehensive Study Orderless recurrent models for multi-label classification

Reference 8

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This paper cites Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning.

Multi-Label Contrastive Learning : A Comprehensive Study Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning

Reference 9

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This paper cites An effective deploy- ment of contrastive learning in multi-label text classification.

Multi-Label Contrastive Learning : A Comprehensive Study An effective deploy- ment of contrastive learning in multi-label text classification

Reference 10

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This paper cites Hierarchical multi-label classifica- tion networks.

Multi-Label Contrastive Learning : A Comprehensive Study Hierarchical multi-label classifica- tion networks

Reference 11

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This paper cites Hierarchy-aware label semantics matching network for hierarchical text classification.

Multi-Label Contrastive Learning : A Comprehensive Study Hierarchy-aware label semantics matching network for hierarchical text classification

Reference 12

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This paper cites Label-specific document representation for multi-label text classification.

Multi-Label Contrastive Learning : A Comprehensive Study Label-specific document representation for multi-label text classification

Reference 13

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This paper cites Ml- decoder: Scalable and versatile classification head.

Multi-Label Contrastive Learning : A Comprehensive Study Ml- decoder: Scalable and versatile classification head

Reference 14

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This paper cites Focal loss for dense object detection.

Multi-Label Contrastive Learning : A Comprehensive Study Focal loss for dense object detection

Reference 15

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This paper cites Asymmetric loss for multi-label classification.

Multi-Label Contrastive Learning : A Comprehensive Study Asymmetric loss for multi-label classification

Reference 16

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This paper cites ZLPR: A Novel Loss for Multi-label Classification.

Multi-Label Contrastive Learning : A Comprehensive Study ZLPR: A Novel Loss for Multi-label Classification

Reference 17

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This paper cites Exploring contrastive learning for long-tailed multi-label text classification.

Multi-Label Contrastive Learning : A Comprehensive Study Exploring contrastive learning for long-tailed multi-label text classification

Reference 18

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This paper cites Multi-label supervised contrastive learning.

Multi-Label Contrastive Learning : A Comprehensive Study Multi-label supervised contrastive learning

Reference 19

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This paper cites Class prototypes based contrastive learning for classifying multi-label and fine-grained educational videos.

Multi-Label Contrastive Learning : A Comprehensive Study Class prototypes based contrastive learning for classifying multi-label and fine-grained educational videos

Reference 20

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This paper cites A simple frame- work for contrastive learning of visual representations.

Multi-Label Contrastive Learning : A Comprehensive Study A simple frame- work for contrastive learning of visual representations

Reference 21

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This paper cites Improving pairwise ranking for multi-label im- age classification.

Multi-Label Contrastive Learning : A Comprehensive Study Improving pairwise ranking for multi-label im- age classification

Reference 22

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Multi-Label Contrastive Learning : A Comprehensive Study Momentum con- trast for unsupervised visual representation learning

Reference 23

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Multi-Label Contrastive Learning : A Comprehensive Study Supervised contrastive learning, 2021

Reference 24

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Multi-Label Contrastive Learning : A Comprehensive Study Dissecting su- pervised contrastive learning

Reference 25

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This paper cites Not all negatives are equal: Label-aware contrastive loss for fine-grained text classification.

Multi-Label Contrastive Learning : A Comprehensive Study Not all negatives are equal: Label-aware contrastive loss for fine-grained text classification

Reference 26

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Multi-Label Contrastive Learning : A Comprehensive Study Label anchored contrastive learning for language understanding

Reference 27

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This paper cites Balanced contrastive learning for long-tailed visual recognition.

Multi-Label Contrastive Learning : A Comprehensive Study Balanced contrastive learning for long-tailed visual recognition

Reference 28

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This paper cites Parametric contrastive learning.

Multi-Label Contrastive Learning : A Comprehensive Study Parametric contrastive learning

Reference 29

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This paper cites Contrastive learning-enhanced nearest neighbor mechanism for multi-label text classification.

Multi-Label Contrastive Learning : A Comprehensive Study Contrastive learning-enhanced nearest neighbor mechanism for multi-label text classification

Reference 30

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Multi-Label Contrastive Learning : A Comprehensive Study Disentangled variational autoencoder based multi-label classification with covariance-aware multivariate probit model

Reference 31

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This paper cites Use all the labels: A hierar- chical multi-label contrastive learning framework.

Multi-Label Contrastive Learning : A Comprehensive Study Use all the labels: A hierar- chical multi-label contrastive learning framework

Reference 32

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This paper cites Incorporating hierarchy into text encoder: a contrastive learning approach for hierarchical text classifi- cation.

Multi-Label Contrastive Learning : A Comprehensive Study Incorporating hierarchy into text encoder: a contrastive learning approach for hierarchical text classifi- cation

Reference 33

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Multi-Label Contrastive Learning : A Comprehensive Study Unresolved cited work

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This paper cites Dao, Ethan Zhao, Dinh Phung, and Jianfei Cai.

Multi-Label Contrastive Learning : A Comprehensive Study Dao, Ethan Zhao, Dinh Phung, and Jianfei Cai

Reference 35

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Multi-Label Contrastive Learning : A Comprehensive Study A unified contrastive loss for self-training

Reference 36

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Multi-Label Contrastive Learning : A Comprehensive Study Improved deep metric learning with multi-class n-pair loss objective

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Multi-Label Contrastive Learning : A Comprehensive Study The pascal visual object classes (voc) challenge

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Multi-Label Contrastive Learning : A Comprehensive Study Microsoft coco: Common objects in context

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Multi-Label Contrastive Learning : A Comprehensive Study Nus-wide: a real-world web image database from national university of singapore

Reference 40

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Multi-Label Contrastive Learning : A Comprehensive Study Rcv1-v2/lyrl2004: the lyrl2004 distribution of the rcv1-v2 text categoriza- tion test collection, 2004

Reference 41

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Multi-Label Contrastive Learning : A Comprehensive Study Hierarchical multi-label classification of text with capsule networks

Reference 42

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Multi-Label Contrastive Learning : A Comprehensive Study RoBERTa: A Robustly Optimized BERT Pretraining Approach

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Multi-Label Contrastive Learning : A Comprehensive Study Decoupled Weight Decay Regularization

Reference 44

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Multi-Label Contrastive Learning : A Comprehensive Study Deep residual learning for image recognition

Reference 45

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Multi-Label Contrastive Learning : A Comprehensive Study Imagenet: A large-scale hierarchical image database

Reference 46

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Multi-Label Contrastive Learning : A Comprehensive Study Understanding contrastive representation learning through alignment and uniformity on the hypersphere

Reference 47

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Multi-Label Contrastive Learning : A Comprehensive Study Newsweeder: Learning to filter netnews

Reference 48

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Multi-Label Contrastive Learning : A Comprehensive Study Semeval-2018 task 1: Affect in tweets

Reference 49

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Pith citing papers

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CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification cites this paper.

CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification Multi-Label Contrastive Learning : A Comprehensive Study

Reference 32

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