GUESS: Generative Uncertainty Ensemble for Self Supervision | Paper Citation Record

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This paper cites Self-supervised visual feature learning with deep neural networks: A survey,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Self-supervised visual feature learning with deep neural networks: A survey,

Reference 1

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This paper cites A unified visual information preservation framework for self-supervised pre-training in medical image analysis,.

GUESS: Generative Uncertainty Ensemble for Self Supervision A unified visual information preservation framework for self-supervised pre-training in medical image analysis,

Reference 2

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This paper cites Deep active ensemble sam- pling for image classification,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Deep active ensemble sam- pling for image classification,

Reference 3

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Observation e77402be-6de9-4436-85ef-4715e10225ea · outbound

This paper cites Deep Bayesian Active Learning, A Brief Survey on Recent Advances.

GUESS: Generative Uncertainty Ensemble for Self Supervision Deep Bayesian Active Learning, A Brief Survey on Recent Advances

Reference 4

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This paper cites A survey on deep semi-supervised learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision A survey on deep semi-supervised learning,

Reference 5

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This paper cites Active uncertainty representation learning: Toward more label efficiency in deep learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Active uncertainty representation learning: Toward more label efficiency in deep learning,

Reference 6

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Observation 679304ea-ebc6-4c32-aa66-ac9b8ba0a0ec · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Barlow twins: Self-supervised learning via redundancy reduction,

Reference 7

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This paper cites Fussl: Fuzzy uncertain self supervised learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Fussl: Fuzzy uncertain self supervised learning,

Reference 8

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This paper cites More synergy, less redundancy: Exploiting joint mutual in- formation for self-supervised learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision More synergy, less redundancy: Exploiting joint mutual in- formation for self-supervised learning,

Reference 9

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Observation 883b1252-e2bf-4b12-9138-27367886b24e · outbound

This paper cites Learning where to learn in cross-view self-supervised learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Learning where to learn in cross-view self-supervised learning,

Reference 10

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This paper cites Representation Learning with Contrastive Predictive Coding.

GUESS: Generative Uncertainty Ensemble for Self Supervision Representation Learning with Contrastive Predictive Coding

Reference 11

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This paper cites Contrastive multiview coding,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Contrastive multiview coding,

Reference 12

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Observation 652e28dd-1290-4e1d-9d5e-51e6f6f33907 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Momentum contrast for unsupervised visual representation learning,

Reference 13

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This paper cites A simple framework for contrastive learning of visual representations,.

GUESS: Generative Uncertainty Ensemble for Self Supervision A simple framework for contrastive learning of visual representations,

Reference 14

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This paper cites Learning representations by maximizing mutual information across views,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Learning representations by maximizing mutual information across views,

Reference 15

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This paper cites Bootstrap your own latent-a new approach to self-supervised learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Bootstrap your own latent-a new approach to self-supervised learning,

Reference 16

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This paper cites Exploring simple siamese representation learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Exploring simple siamese representation learning,

Reference 17

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This paper cites Understanding self-supervised learn- ing dynamics without contrastive pairs,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Understanding self-supervised learn- ing dynamics without contrastive pairs,

Reference 18

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Observation ce7f272b-3183-4c95-bd14-21ed0e52daa1 · outbound

This paper cites Whitening for self-supervised representation learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Whitening for self-supervised representation learning,

Reference 19

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Observation c8a36c39-26a5-4208-ae9a-7e4d1a27261f · outbound

This paper cites Dense contrastive learning for self-supervised visual pre-training,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Dense contrastive learning for self-supervised visual pre-training,

Reference 20

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Observation 802bb068-86be-4010-956b-9503f5bacfe2 · outbound

This paper cites Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning,

Reference 21

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This paper cites Spatially consistent representation learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Spatially consistent representation learning,

Reference 22

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This paper cites Region similarity representation learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Region similarity representation learning,

Reference 23

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Observation 62c47a3e-a1f6-444b-83ed-9a4488a4e649 · outbound

This paper cites Self-supervised models are continual learners,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Self-supervised models are continual learners,

Reference 24

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This paper cites Lex fridman podcast, (MIT AI podcast). episode # 258.

GUESS: Generative Uncertainty Ensemble for Self Supervision Lex fridman podcast, (MIT AI podcast). episode # 258

Reference 25

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This paper cites Using self- supervised learning can improve model robustness and uncertainty,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Using self- supervised learning can improve model robustness and uncertainty,

Reference 26

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This paper cites On the uncertainty of self-supervised monocular depth estimation,.

GUESS: Generative Uncertainty Ensemble for Self Supervision On the uncertainty of self-supervised monocular depth estimation,

Reference 27

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This paper cites Exploiting unlabeled data in cnns by self-supervised learning to rank,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Exploiting unlabeled data in cnns by self-supervised learning to rank,

Reference 28

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Observation 779b47f7-42b7-4ba8-a7de-62a44447a97a · outbound

This paper cites Self-supervised low-light image enhancement using discrepant untrained network priors,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Self-supervised low-light image enhancement using discrepant untrained network priors,

Reference 29

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This paper cites Self-supervised learning by estimating twin class distribution,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Self-supervised learning by estimating twin class distribution,

Reference 30

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This paper cites Adversarial Feature Learning.

GUESS: Generative Uncertainty Ensemble for Self Supervision Adversarial Feature Learning

Reference 31

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This paper cites Unsupervised learning of visual features by contrasting cluster assign- ments,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Unsupervised learning of visual features by contrasting cluster assign- ments,

Reference 32

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This paper cites Deep clustering for unsupervised learning of visual features,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Deep clustering for unsupervised learning of visual features,

Reference 33

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This paper cites Emerging properties in self-supervised vision transformers,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Emerging properties in self-supervised vision transformers,

Reference 34

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This paper cites Improved deep metric learning with multi-class n-pair loss objective,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Improved deep metric learning with multi-class n-pair loss objective,

Reference 35

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This paper cites Unsupervised learning of visual representations using videos,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Unsupervised learning of visual representations using videos,

Reference 36

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This paper cites Learning multiple layers of features from tiny images,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Learning multiple layers of features from tiny images,

Reference 37

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This paper cites Tiny imagenet visual recognition challenge,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Tiny imagenet visual recognition challenge,

Reference 38

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This paper cites Deep residual learning for image recognition,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Deep residual learning for image recognition,

Reference 39

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This paper cites Adam: A Method for Stochastic Optimization.

GUESS: Generative Uncertainty Ensemble for Self Supervision Adam: A Method for Stochastic Optimization

Reference 40

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This paper cites solo-learn: A library of self-supervised methods for visual representation learning.

GUESS: Generative Uncertainty Ensemble for Self Supervision solo-learn: A library of self-supervised methods for visual representation learning

Reference 41

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This paper cites Directional self-supervised learning for heavy image augmentations,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Directional self-supervised learning for heavy image augmentations,

Reference 42

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This paper cites Shuffle and learn: unsupervised learning using temporal order verification,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Shuffle and learn: unsupervised learning using temporal order verification,

Reference 43

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Observation 2a25f503-29b5-43b2-b320-46e3326e913c · outbound

This paper cites Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods.

GUESS: Generative Uncertainty Ensemble for Self Supervision Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods

Reference 44

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This paper cites Uncertainty-aware self-supervised learning of spatial perception tasks,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Uncertainty-aware self-supervised learning of spatial perception tasks,

Reference 45

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This paper cites Credal self-supervised learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Credal self-supervised learning,

Reference 46

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Observation d8aa7ddb-5f55-41d4-8721-7c4cf4d44fe0 · outbound

This paper cites Cliquecnn: Deep unsupervised exemplar learning,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Cliquecnn: Deep unsupervised exemplar learning,

Reference 47

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This paper cites Invariant information clustering for unsupervised image classification and segmentation,.

GUESS: Generative Uncertainty Ensemble for Self Supervision Invariant information clustering for unsupervised image classification and segmentation,

Reference 48

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This paper cites The information bottleneck method.

GUESS: Generative Uncertainty Ensemble for Self Supervision The information bottleneck method

Reference 49

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GUESS: Generative Uncertainty Ensemble for Self Supervision Unresolved cited work

Reference 50

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GUESS: Generative Uncertainty Ensemble for Self Supervision Unresolved cited work

Reference 51

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Observation 7a59caf7-1c7f-405a-9928-51ea5afdbfd4 · outbound

This paper cites this work proposes the use of credal sets to model uncertainty in pseudo-labels and hence reduce calibration errors in SSL approach.

GUESS: Generative Uncertainty Ensemble for Self Supervision this work proposes the use of credal sets to model uncertainty in pseudo-labels and hence reduce calibration errors in SSL approach

Reference 52

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