Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning | Paper Citation Record

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This paper cites A brief introduction to weakly supervised learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning A brief introduction to weakly supervised learning,

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This paper cites Multiple instance classification: Review, taxonomy and comparative study,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Multiple instance classification: Review, taxonomy and comparative study,

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This paper cites Multiple instance learning: A survey of problem characteristics and applications,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Multiple instance learning: A survey of problem characteristics and applications,

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This paper cites Attention-based deep multiple instance learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Attention-based deep multiple instance learning,

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This paper cites Revisiting multiple instance neural networks,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Revisiting multiple instance neural networks,

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This paper cites Multi- instance causal representation learning for instance label predic- tion and out-of-distribution generalization,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Multi- instance causal representation learning for instance label predic- tion and out-of-distribution generalization,

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This paper cites DTFD-MIL: Double-tier feature distillation multiple instance learning for histopathology whole slide image classifi- cation,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning DTFD-MIL: Double-tier feature distillation multiple instance learning for histopathology whole slide image classifi- cation,

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This paper cites Incorporating probabilistic domain knowledge into deep multiple instance learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Incorporating probabilistic domain knowledge into deep multiple instance learning,

Reference 8

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This paper cites Data- driven knowledge fusion for deep multi-instance learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Data- driven knowledge fusion for deep multi-instance learning,

Reference 9

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This paper cites Learning from partial labels,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Learning from partial labels,

Reference 10

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This paper cites Exploiting class activation value for partial-label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Exploiting class activation value for partial-label learning,

Reference 11

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This paper cites Partial label learning with semantic label representations,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Partial label learning with semantic label representations,

Reference 12

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This paper cites A unifying probabilistic framework for partially labeled data learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning A unifying probabilistic framework for partially labeled data learning,

Reference 13

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This paper cites Learning with partial labels from semi-supervised perspective,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Learning with partial labels from semi-supervised perspective,

Reference 14

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This paper cites Progressive purification for instance-dependent partial label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Progressive purification for instance-dependent partial label learning,

Reference 15

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This paper cites Distilling reliable knowledge for instance-dependent partial label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Distilling reliable knowledge for instance-dependent partial label learning,

Reference 16

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This paper cites Partial label causal representation learning for instance-dependent supervision and domain generalization,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Partial label causal representation learning for instance-dependent supervision and domain generalization,

Reference 17

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This paper cites Disambiguated attention embedding for multi-instance partial-label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Disambiguated attention embedding for multi-instance partial-label learning,

Reference 18

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This paper cites Multi-instance partial-label learning: Towards exploiting dual inexact supervision,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Multi-instance partial-label learning: Towards exploiting dual inexact supervision,

Reference 19

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This paper cites Clinical-grade computational pathology using weakly supervised deep learning on whole slide images,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Clinical-grade computational pathology using weakly supervised deep learning on whole slide images,

Reference 20

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This paper cites Crowdsourcing of histological image labeling and object delineation by medical students,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Crowdsourcing of histological image labeling and object delineation by medical students,

Reference 21

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This paper cites Exploiting conjugate label information for multi-instance partial-label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Exploiting conjugate label information for multi-instance partial-label learning,

Reference 22

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This paper cites Multi-instance partial-label learning with margin adjustment,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Multi-instance partial-label learning with margin adjustment,

Reference 23

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This paper cites Pro- gressive identification of true labels for partial-label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Pro- gressive identification of true labels for partial-label learning,

Reference 24

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This paper cites Solving the multiple instance problem with axis-parallel rectangles,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Solving the multiple instance problem with axis-parallel rectangles,

Reference 25

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This paper cites Multi-instance learning by treating instances as non-i.i.d. samples,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Multi-instance learning by treating instances as non-i.i.d. samples,

Reference 26

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This paper cites Multiple instance active learning for object detection,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Multiple instance active learning for object detection,

Reference 27

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This paper cites Unbiased multiple instance learning for weakly supervised video anomaly detection,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Unbiased multiple instance learning for weakly supervised video anomaly detection,

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This paper cites Loss-based attention for deep multiple instance learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Loss-based attention for deep multiple instance learning,

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This paper cites Bayes-MIL: A new probabilistic perspective on attention-based multiple instance learning for whole slide im- ages,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Bayes-MIL: A new probabilistic perspective on attention-based multiple instance learning for whole slide im- ages,

Reference 30

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This paper cites CAMIL: context-aware multiple instance learning for cancer detection and subtyping in whole slide images,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning CAMIL: context-aware multiple instance learning for cancer detection and subtyping in whole slide images,

Reference 31

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This paper cites Inherently interpretable time series classification via multiple instance learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Inherently interpretable time series classification via multiple instance learning,

Reference 32

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This paper cites GM-PLL: Graph matching based partial label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning GM-PLL: Graph matching based partial label learning,

Reference 33

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This paper cites Deep discriminative CNN with temporal ensembling for ambiguously- labeled image classification,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Deep discriminative CNN with temporal ensembling for ambiguously- labeled image classification,

Reference 34

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This paper cites A conditional multinomial mixture model for superset label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning A conditional multinomial mixture model for superset label learning,

Reference 35

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This paper cites Tuning the Right Foundation Models is What you Need for Partial Label Learning.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Tuning the Right Foundation Models is What you Need for Partial Label Learning

Reference 36

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This paper cites Rank-loss support instance machines for MIML instance annotation,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Rank-loss support instance machines for MIML instance annotation,

Reference 37

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This paper cites Semi-supervised partial label learning via confidence-rated margin maximization,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Semi-supervised partial label learning via confidence-rated margin maximization,

Reference 38

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This paper cites Adaptive graph guided disambiguation for partial label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Adaptive graph guided disambiguation for partial label learning,

Reference 39

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This paper cites Provably consistent partial-label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Provably consistent partial-label learning,

Reference 40

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This paper cites Leveraged weighted loss for partial label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Leveraged weighted loss for partial label learning,

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This paper cites Realistic evaluation of deep partial-label learning algorithms,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Realistic evaluation of deep partial-label learning algorithms,

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This paper cites ProMIPL: A probabilistic generative model for multi-instance partial-label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning ProMIPL: A probabilistic generative model for multi-instance partial-label learning,

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This paper cites Fast multi-instance partial- label learning,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Fast multi-instance partial- label learning,

Reference 44

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This paper cites On learning latent models with multi-instance weak supervision,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning On learning latent models with multi-instance weak supervision,

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Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning On calibration of modern neural networks,

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This paper cites When does label smoothing help?.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning When does label smoothing help?

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This paper cites Rethinking calibration of deep neural networks: Do not be afraid of overconfidence,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Rethinking calibration of deep neural networks: Do not be afraid of overconfidence,

Reference 48

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Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning On mixup training: Improved calibration and predictive uncertainty for deep neural networks,

Reference 49

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Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning When and how mixup improves calibration,

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This paper cites Calibrating deep neural networks using focal loss,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Calibrating deep neural networks using focal loss,

Reference 51

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Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Dual focal loss for calibration,

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This paper cites Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods,

Reference 53

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This paper cites Post-hoc uncertainty calibration for domain drift scenarios,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Post-hoc uncertainty calibration for domain drift scenarios,

Reference 54

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This paper cites On the pitfall of mixup for uncertainty calibration,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning On the pitfall of mixup for uncertainty calibration,

Reference 55

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This paper cites Calibration bottleneck: Over- compressed representations are less calibratable,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Calibration bottleneck: Over- compressed representations are less calibratable,

Reference 56

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This paper cites Focal loss for dense object detection,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Focal loss for dense object detection,

Reference 57

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This paper cites Gradient-based learning applied to document recognition,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Gradient-based learning applied to document recognition,

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This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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This paper cites Multiple-instance active learn- ing,.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Multiple-instance active learn- ing,

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