Confidence Regularized Self-Training | Paper Citation Record

Observation 3f3ed13e-da30-4afb-86b4-cf55197f30a6 · outbound

This paper cites Semi-supervised logistic regression.

Confidence Regularized Self-Training Semi-supervised logistic regression

Reference 1

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Observation a5e9224e-92ca-41df-85ff-b9c8bc77a514 · outbound

This paper cites Label Refinery: Improving ImageNet Classification through Label Progression.

Confidence Regularized Self-Training Label Refinery: Improving ImageNet Classification through Label Progression

Reference 2

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Observation dc0b7430-2a7b-442a-a760-6a1987403efe · outbound

This paper cites Convex optimiza- tion.

Confidence Regularized Self-Training Convex optimiza- tion

Reference 3

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Observation 6e628762-a2f1-4511-bf97-72b3d657d909 · outbound

This paper cites Open set domain adaptation for image and action recognition.

Confidence Regularized Self-Training Open set domain adaptation for image and action recognition

Reference 4

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Observation 31fd320d-a299-4f53-85f7-243c672794d1 · outbound

This paper cites Progressive feature alignment for unsupervised do- main adaptation.

Confidence Regularized Self-Training Progressive feature alignment for unsupervised do- main adaptation

Reference 5

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Observation f95c11bc-6691-4836-9246-13b5075a64e0 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs.

Confidence Regularized Self-Training Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 6

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Observation 5a0dfc3a-93f8-4694-a683-d3f27a340de2 · outbound

This paper cites MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems.

Confidence Regularized Self-Training MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

Reference 7

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This paper cites Learning semantic segmentation from synthetic data: A ge- ometrically guided input-output adaptation approach.

Confidence Regularized Self-Training Learning semantic segmentation from synthetic data: A ge- ometrically guided input-output adaptation approach

Reference 8

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Observation 2b9d297c-3f67-4ca4-81a7-16df7ff6e23f · outbound

This paper cites Domain adaptive faster r-cnn for object de- tection in the wild.

Confidence Regularized Self-Training Domain adaptive faster r-cnn for object de- tection in the wild

Reference 9

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Observation 60bbc543-509e-4ac6-b499-5d5f05dac84a · outbound

This paper cites No more discrimi- nation: Cross city adaptation of road scene segmenters.

Confidence Regularized Self-Training No more discrimi- nation: Cross city adaptation of road scene segmenters

Reference 10

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Observation 57002139-f85a-4102-860b-b7b105003ccf · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Confidence Regularized Self-Training The cityscapes dataset for semantic urban scene understanding

Reference 11

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Observation 4f183bee-54b4-44df-a8f2-e2fd05c2e8c7 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Confidence Regularized Self-Training Imagenet: A large-scale hierarchical image database

Reference 12

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Observation 271726d1-54a3-4534-984b-bbfc630cad1b · outbound

This paper cites Domain styliza- tion: A fast covariance matching framework towards domain adaptation.

Confidence Regularized Self-Training Domain styliza- tion: A fast covariance matching framework towards domain adaptation

Reference 13

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Observation f28706b6-3294-4f37-8ca5-a6cbd6e5603f · outbound

This paper cites Self-ensembling for visual domain adaptation.

Confidence Regularized Self-Training Self-ensembling for visual domain adaptation

Reference 14

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Observation 454f14b2-db38-49cc-a197-787c3e61eedd · outbound

This paper cites Unsupervised domain adaptation by backpropagation.

Confidence Regularized Self-Training Unsupervised domain adaptation by backpropagation

Reference 15

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Observation 586d5b01-0b6e-4a82-8a3b-8241ab143471 · outbound

This paper cites Domain-adversarial train- ing of neural networks.

Confidence Regularized Self-Training Domain-adversarial train- ing of neural networks

Reference 16

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Observation 088a3787-26f3-4b5c-ae8b-34aa1ee7cdfa · outbound

This paper cites DLOW: Domain ﬂow for adaptation and generalization.

Confidence Regularized Self-Training DLOW: Domain ﬂow for adaptation and generalization

Reference 17

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Observation 5f036fba-f33b-49e6-a8cb-3de227dc2da6 · outbound

This paper cites Deep Learning.

Confidence Regularized Self-Training Deep Learning

Reference 18

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Observation 1203092f-b2c5-4310-9880-f416c0747ce3 · outbound

This paper cites Semi-supervised learning by entropy minimization.

Confidence Regularized Self-Training Semi-supervised learning by entropy minimization

Reference 19

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Observation bc770475-790b-4bcf-b85a-497bf49f34ab · outbound

This paper cites Weinberger.

Confidence Regularized Self-Training Weinberger

Reference 20

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Observation 56e72a30-c88d-445e-8a47-eba0234de78d · outbound

This paper cites Unsupervised domain adaptation via calibrat- ing uncertainties.

Confidence Regularized Self-Training Unsupervised domain adaptation via calibrat- ing uncertainties

Reference 21

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Observation 3fa91c94-c728-463c-9a34-66262ce32f59 · outbound

This paper cites Deep residual learning for image recognition.

Confidence Regularized Self-Training Deep residual learning for image recognition

Reference 22

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Observation 0257e4d1-bcdc-47fe-94bb-aa092fc084b0 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Confidence Regularized Self-Training Distilling the Knowledge in a Neural Network

Reference 23

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Observation d6e1c5e4-e970-4b62-a41d-60ce41d80179 · outbound

This paper cites Cycada: Cycle-consistent adversarial domain adapta- tion.

Confidence Regularized Self-Training Cycada: Cycle-consistent adversarial domain adapta- tion

Reference 24

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Observation 19e30cb8-3e47-42d0-97e0-57d0792883d4 · outbound

This paper cites Cross-domain weakly-supervised object de- tection through progressive domain adaptation.

Confidence Regularized Self-Training Cross-domain weakly-supervised object de- tection through progressive domain adaptation

Reference 25

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Observation 5f44928c-53d0-4aa2-a5fd-740ac5a8f3d2 · outbound

This paper cites Unsupervised visual domain adaptation: A deep max-margin Gaussian process approach.

Confidence Regularized Self-Training Unsupervised visual domain adaptation: A deep max-margin Gaussian process approach

Reference 26

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Observation 5dd90c2d-9008-4f54-af04-7807347e8027 · outbound

This paper cites Temporal ensembling for semi- supervised learning.

Confidence Regularized Self-Training Temporal ensembling for semi- supervised learning

Reference 27

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Observation b6638d44-f591-47ac-a44f-872b434a640f · outbound

This paper cites Sliced wasserstein discrepancy for unsu- pervised domain adaptation.

Confidence Regularized Self-Training Sliced wasserstein discrepancy for unsu- pervised domain adaptation

Reference 28

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Observation 8a392608-e325-4c00-a32b-5719154cefa8 · outbound

This paper cites Pseudo-label: The simple and efﬁcient semi-supervised learning method for deep neural networks.

Confidence Regularized Self-Training Pseudo-label: The simple and efﬁcient semi-supervised learning method for deep neural networks

Reference 29

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Observation fdf1d454-9348-4e4d-b8a4-021bd43a561d · outbound

This paper cites Microsoft COCO: Common objects in context.

Confidence Regularized Self-Training Microsoft COCO: Common objects in context

Reference 30

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Observation 3e8da626-537f-4982-85ab-901191ef1b21 · outbound

This paper cites Feature-level franken- stein: Eliminating variations for discriminative recognition.

Confidence Regularized Self-Training Feature-level franken- stein: Eliminating variations for discriminative recognition

Reference 31

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Observation e8328cc0-a287-4452-8551-125048a22a5b · outbound

This paper cites an unresolved cited work.

Confidence Regularized Self-Training Unresolved cited work

Reference 32

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Observation 606bc4f8-6040-47ac-80b5-6dfc5696aad1 · outbound

This paper cites Learning transferable features with deep adaptation networks.

Confidence Regularized Self-Training Learning transferable features with deep adaptation networks

Reference 33

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Observation a7088f9e-7b0b-42da-b8f3-827cdd13ee4d · outbound

This paper cites Conditional adversarial domain adapta- tion.

Confidence Regularized Self-Training Conditional adversarial domain adapta- tion

Reference 34

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Observation 24ae4aa1-a5f3-40a9-8116-ad7f9ab8e0bd · outbound

This paper cites Unsupervised domain adaptation with residual trans- fer networks.

Confidence Regularized Self-Training Unsupervised domain adaptation with residual trans- fer networks

Reference 35

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Observation 5bcb0a72-7705-48f2-9503-de84a923ffb7 · outbound

This paper cites Deep transfer learning with joint adaptation net- works.

Confidence Regularized Self-Training Deep transfer learning with joint adaptation net- works

Reference 36

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Observation 28662ff9-6b7a-4038-bf8e-9c1a7834fb21 · outbound

This paper cites Smooth neighbors on teacher graphs for semi-supervised learning.

Confidence Regularized Self-Training Smooth neighbors on teacher graphs for semi-supervised learning

Reference 37

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Observation a0a9f460-43fe-483b-a4ad-646e42a0788b · outbound

This paper cites Image to image translation for domain adaptation.

Confidence Regularized Self-Training Image to image translation for domain adaptation

Reference 38

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Observation 280a6131-4070-4839-9ff0-fc2759a8ccce · outbound

This paper cites Learning with noisy labels.

Confidence Regularized Self-Training Learning with noisy labels

Reference 39

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Observation c3a806e7-87db-4bc1-8cfe-cf8349e0167e · outbound

This paper cites Open set domain adaptation.

Confidence Regularized Self-Training Open set domain adaptation

Reference 40

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Observation d55e6686-c313-4b15-a0b8-8d626df87791 · outbound

This paper cites Automatic differentiation in pytorch.

Confidence Regularized Self-Training Automatic differentiation in pytorch

Reference 41

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Observation ddb0178b-2d03-45f5-a20c-1acb191b98c0 · outbound

This paper cites Visda: A synthetic-to-real benchmark for visual do- main adaptation.

Confidence Regularized Self-Training Visda: A synthetic-to-real benchmark for visual do- main adaptation

Reference 42

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Observation a34f951a-48c8-4dec-ab1c-7e72af75f60d · outbound

This paper cites Regularizing neural networks by penalizing conﬁdent output distributions.

Confidence Regularized Self-Training Regularizing neural networks by penalizing conﬁdent output distributions

Reference 43

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Observation 50bd8bfa-64f6-41c8-97d4-2fbb080b8659 · outbound

This paper cites Unsupervised domain adaptation with similarity learning.

Confidence Regularized Self-Training Unsupervised domain adaptation with similarity learning

Reference 44

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Observation 4b227bbe-0c3e-48c1-a118-80c0f074fa61 · outbound

This paper cites Train- ing deep neural networks on noisy labels with bootstrapping.

Confidence Regularized Self-Training Train- ing deep neural networks on noisy labels with bootstrapping

Reference 45

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Observation 362bfe26-a16a-4ce2-8751-442d97a4d7ef · outbound

This paper cites Playing for data: Ground truth from computer games.

Confidence Regularized Self-Training Playing for data: Ground truth from computer games

Reference 46

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Observation 48793531-5a3e-4206-b071-5a9a0ecfae31 · outbound

This paper cites The SYNTHIA dataset: A large collection of synthetic images for semantic segmen- tation of urban scenes.

Confidence Regularized Self-Training The SYNTHIA dataset: A large collection of synthetic images for semantic segmen- tation of urban scenes

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:04:11.620155Z

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Observation 979905bf-47e0-4e16-bfab-6ee28ad7c0c7 · outbound

This paper cites Adapting visual category models to new domains.

Confidence Regularized Self-Training Adapting visual category models to new domains

Reference 48

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Observation 65bc478e-7e8b-4ea8-a01a-3d469b768a99 · outbound

This paper cites Asymmetric tri-training for unsupervised domain adaptation.

Confidence Regularized Self-Training Asymmetric tri-training for unsupervised domain adaptation

Reference 49

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Observation 2983b5f8-9a1b-4594-bdd2-ddbc7b65f3d3 · outbound

This paper cites Adversarial dropout regularization.

Confidence Regularized Self-Training Adversarial dropout regularization

Reference 50

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Observation 0963cecc-8fdf-42c0-b3c9-f3b2530e9300 · outbound

This paper cites Maximum classiﬁer discrepancy for unsuper- vised domain adaptation.

Confidence Regularized Self-Training Maximum classiﬁer discrepancy for unsuper- vised domain adaptation

Reference 51

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Observation 1f7edc46-6ae0-49c2-a15f-caa12805a3ee · outbound

This paper cites Open set domain adaptation by backpropa- gation.

Confidence Regularized Self-Training Open set domain adaptation by backpropa- gation

Reference 52

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Source-reported events for the cited work

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Observation c596d32b-cda0-4ef3-8d8e-582bf7d1c5ce · outbound

This paper cites Generate to adapt: Aligning domains using generative adversarial networks.

Confidence Regularized Self-Training Generate to adapt: Aligning domains using generative adversarial networks

Reference 53

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Observation d638c38b-ff61-4a8d-b241-9bfd102f2911 · outbound

This paper cites A dirt-t approach to unsupervised domain adaptation.

Confidence Regularized Self-Training A dirt-t approach to unsupervised domain adaptation

Reference 54

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Observation 453ad21f-af74-4bf3-a111-1ad9eae1a921 · outbound

This paper cites Training Convolutional Networks with Noisy Labels.

Confidence Regularized Self-Training Training Convolutional Networks with Noisy Labels

Reference 55

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Observation 7df3e681-d4b4-44b0-9ab7-1e1fafa2e007 · outbound

This paper cites Deep coral: Correlation alignment for deep domain adaptation.

Confidence Regularized Self-Training Deep coral: Correlation alignment for deep domain adaptation

Reference 56

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Observation ca87a5fe-7e51-403d-97ba-9132311b61f4 · outbound

This paper cites Rethinking the inception ar- chitecture for computer vision.

Confidence Regularized Self-Training Rethinking the inception ar- chitecture for computer vision

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:04:11.509054Z

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Observation c577e7cb-9424-4853-8823-c381b9aea630 · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

Confidence Regularized Self-Training Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 58

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0e4230a-26a8-457d-8ded-d258d572678f · outbound

This paper cites Self-labeled techniques for semi-supervised learning: taxon- omy, software and empirical study.Knowledge and Informa- tion Systems, 2015.

Confidence Regularized Self-Training Self-labeled techniques for semi-supervised learning: taxon- omy, software and empirical study.Knowledge and Informa- tion Systems, 2015

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:04:11.489873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0104c046-74e0-4cf5-ae49-16f21abf149b · outbound

This paper cites Learning to adapt structured output space for semantic seg- mentation.

Confidence Regularized Self-Training Learning to adapt structured output space for semantic seg- mentation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:04:11.477375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ba503377-a96e-4ea8-918d-55d48286384d · outbound

This paper cites Adversarial discriminative domain adaptation.

Confidence Regularized Self-Training Adversarial discriminative domain adaptation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:04:11.465122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 83dc3c85-dd44-4266-9f23-ea6da6945559 · outbound

This paper cites Deep Domain Confusion: Maximizing for Domain Invariance.

Confidence Regularized Self-Training Deep Domain Confusion: Maximizing for Domain Invariance

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-14T11:04:11.265452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ecdcfbd-b706-4005-ae22-82031b5e02ae · outbound

This paper cites Advent: Adversarial entropy mini- mization for domain adaptation in semantic segmentation.

Confidence Regularized Self-Training Advent: Adversarial entropy mini- mization for domain adaptation in semantic segmentation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:04:11.452386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0c35211e-d54a-426d-82ea-03d087d5983d · outbound

This paper cites Dcan: Dual channel-wise alignment networks for un- supervised scene adaptation.

Confidence Regularized Self-Training Dcan: Dual channel-wise alignment networks for un- supervised scene adaptation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:04:11.438868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eb683b32-be86-4c8c-8caa-4e98a04e550b · outbound

This paper cites Unsupervised word sense disambiguation rivaling supervised methods.

Confidence Regularized Self-Training Unsupervised word sense disambiguation rivaling supervised methods

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:04:11.425684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e9893a43-518d-4c2f-9f75-19721525dc1a · outbound

This paper cites an unresolved cited work.

Confidence Regularized Self-Training Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-14T11:04:11.412556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 53e3d760-017e-43aa-a269-990d3480fe0a · outbound

This paper cites Fully convolutional adaptation networks for semantic segmentation.

Confidence Regularized Self-Training Fully convolutional adaptation networks for semantic segmentation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:04:11.399925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cecec1d1-c92f-42a4-90f3-49b6cbddb6bf · outbound

This paper cites expectation.

Confidence Regularized Self-Training expectation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:04:11.386705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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