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Paper Citation Record · LEDGER

Exploring Spatial Diversity for Region-based Active Learning

As of 15 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.17367.

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

pith.paper-citation-record.v1
2507.17367 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:56:38.516297Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation 2b9150a4-7da8-4cc9-b260-c6f51522778c · outbound

This paper cites Settles, Active Learning, ser.

Exploring Spatial Diversity for Region-based Active Learning Settles, Active Learning, ser

Reference 1

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Observation a6c69111-a592-4b31-84f1-fab7f4ea84ac · outbound

This paper cites Multi-class active learning by uncertainty sampling with diversity maximization,.

Exploring Spatial Diversity for Region-based Active Learning Multi-class active learning by uncertainty sampling with diversity maximization,

Reference 2

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Observation 39551ad2-6d57-4361-9e95-29103edf1951 · outbound

This paper cites Deep bayesian active learning with image data,.

Exploring Spatial Diversity for Region-based Active Learning Deep bayesian active learning with image data,

Reference 3

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Observation 5419578e-7792-4ed7-8331-b92d25492467 · outbound

This paper cites Active learning for convolutional neural networks: A core-set approach,.

Exploring Spatial Diversity for Region-based Active Learning Active learning for convolutional neural networks: A core-set approach,

Reference 4

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Observation 84807920-ab45-4b02-bdf7-3ca1ec30b39a · outbound

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

Exploring Spatial Diversity for Region-based Active Learning The cityscapes dataset for semantic urban scene understanding,

Reference 5

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Observation a7abdf1f-796d-45d9-abbe-43f9b3847422 · outbound

This paper cites CEREALS - cost-effective region-based active learning for semantic segmentation,.

Exploring Spatial Diversity for Region-based Active Learning CEREALS - cost-effective region-based active learning for semantic segmentation,

Reference 6

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Observation cbc12ab5-5cd4-4ad4-a99b-f973755ccef5 · outbound

This paper cites Rein- forced Active Learning for Image Segmentation,.

Exploring Spatial Diversity for Region-based Active Learning Rein- forced Active Learning for Image Segmentation,

Reference 7

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Observation 9c827cef-eb6e-45ef-b005-2ac2c83f1331 · outbound

This paper cites A sequential algorithm for training text classifiers,.

Exploring Spatial Diversity for Region-based Active Learning A sequential algorithm for training text classifiers,

Reference 8

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Observation 0d3670e5-23ea-4d3b-ab71-da08d4b414ec · outbound

This paper cites Multi-class active learning for image classification,.

Exploring Spatial Diversity for Region-based Active Learning Multi-class active learning for image classification,

Reference 9

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Observation ab90c828-0bd2-402e-ae50-ae1d94a59d7b · outbound

This paper cites Bayesian Active Learning for Classification and Preference Learning.

Exploring Spatial Diversity for Region-based Active Learning Bayesian Active Learning for Classification and Preference Learning

Reference 10

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Observation 0b5cecd0-df22-49bc-868e-4b4e20af4cb5 · outbound

This paper cites Active learning using pre- clustering,.

Exploring Spatial Diversity for Region-based Active Learning Active learning using pre- clustering,

Reference 11

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Observation d4b96095-f95c-4449-9edb-2ecdee0da533 · outbound

This paper cites Querying discriminative and representative samples for batch mode active learning,.

Exploring Spatial Diversity for Region-based Active Learning Querying discriminative and representative samples for batch mode active learning,

Reference 12

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This paper cites Active learning for semantic segmentation with expected change,.

Exploring Spatial Diversity for Region-based Active Learning Active learning for semantic segmentation with expected change,

Reference 13

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This paper cites The power of ensembles for active learning in image classification,.

Exploring Spatial Diversity for Region-based Active Learning The power of ensembles for active learning in image classification,

Reference 14

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This paper cites Learning loss for active learning.

Exploring Spatial Diversity for Region-based Active Learning Learning loss for active learning

Reference 15

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Observation 736d3022-e84d-42d1-bdfb-099980c8b156 · outbound

This paper cites Fine-tuning convolutional neural networks for biomedical image analysis: Actively and incrementally,.

Exploring Spatial Diversity for Region-based Active Learning Fine-tuning convolutional neural networks for biomedical image analysis: Actively and incrementally,

Reference 16

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Exploring Spatial Diversity for Region-based Active Learning Active image segmentation propagation,

Reference 17

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Observation 30164c99-ac3e-429e-957d-b600fb4b19ec · outbound

This paper cites Suggestive annotation: A deep active learning framework for biomedical image segmentation,.

Exploring Spatial Diversity for Region-based Active Learning Suggestive annotation: A deep active learning framework for biomedical image segmentation,

Reference 18

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Observation f10dd420-c010-4dc8-ad92-f035c6551893 · outbound

This paper cites Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network,.

Exploring Spatial Diversity for Region-based Active Learning Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network,

Reference 19

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Observation 378f5efa-fd48-42da-8f61-77b57f3a583f · outbound

This paper cites Variational adversarial active learning,.

Exploring Spatial Diversity for Region-based Active Learning Variational adversarial active learning,

Reference 20

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Observation b890eec5-9925-4938-923e-43e0c4a69210 · outbound

This paper cites Region-based active learning for efficient labeling in se- mantic segmentation,.

Exploring Spatial Diversity for Region-based Active Learning Region-based active learning for efficient labeling in se- mantic segmentation,

Reference 21

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This paper cites Viewal: Active learning with viewpoint entropy for semantic segmentation,.

Exploring Spatial Diversity for Region-based Active Learning Viewal: Active learning with viewpoint entropy for semantic segmentation,

Reference 22

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Observation cc5ee9e0-4289-4caf-92dd-ed496e883e86 · outbound

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Exploring Spatial Diversity for Region-based Active Learning Heuristic and special case algorithms for dispersion problems,

Reference 23

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Observation d9345f90-9181-4449-9e61-602e46dbb510 · outbound

This paper cites An axiomatic approach for result diversi- fication,.

Exploring Spatial Diversity for Region-based Active Learning An axiomatic approach for result diversi- fication,

Reference 24

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Observation ab0d8052-b473-4e96-bcee-68b82a26a6bc · outbound

This paper cites A Survey of Deep Active Learning.

Exploring Spatial Diversity for Region-based Active Learning A Survey of Deep Active Learning

Reference 25

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Exploring Spatial Diversity for Region-based Active Learning The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results,

Reference 26

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Exploring Spatial Diversity for Region-based Active Learning Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation,

Reference 27

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Exploring Spatial Diversity for Region-based Active Learning Encoder- decoder with atrous separable convolution for semantic image segmen- tation,

Reference 28

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Exploring Spatial Diversity for Region-based Active Learning Xception: Deep learning with depthwise separable convo- lutions,

Reference 29

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Exploring Spatial Diversity for Region-based Active Learning Imagenet: A large-scale hierarchical image database,

Reference 30

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Exploring Spatial Diversity for Region-based Active Learning Feature pyramid networks for object detection,

Reference 31

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Exploring Spatial Diversity for Region-based Active Learning Playing for data: Ground truth from computer games,

Reference 32

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Exploring Spatial Diversity for Region-based Active Learning Max-sum diversification, monotone submodular functions and dynamic updates,

Reference 33

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Pith citing papers

No inbound Pith citation observations are available.