Extending Dataset Pruning to Object Detection: A Variance-based Approach | Paper Citation Record

Observation 995643e3-21ff-462d-990e-9860e44b05f1 · outbound

This paper cites Balancing feature similarity and label vari- ability for optimal size-aware one-shot subset selection.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Balancing feature similarity and label vari- ability for optimal size-aware one-shot subset selection

Reference 1

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Observation 2fa0cf97-2027-4f2c-9a84-84aa6613fc2d · outbound

This paper cites Agarwal, Sariel Har-Peled, and Kasturi R.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Agarwal, Sariel Har-Peled, and Kasturi R

Reference 2

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Observation 60619b4d-bae1-4182-9ac3-c329b3a2df69 · outbound

This paper cites Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance

Reference 3

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Observation 5bf40903-b73b-4e5f-8e88-fb3fef8f6a44 · outbound

This paper cites Smaller core-sets for balls.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Smaller core-sets for balls

Reference 4

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Observation bff07c94-fad4-4f16-b755-505a13486036 · outbound

This paper cites Anchor pruning for object detection.Computer Vision and Image Understanding, 221:103445, 2022.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Anchor pruning for object detection.Computer Vision and Image Understanding, 221:103445, 2022

Reference 5

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Observation 95fb3f8f-174d-4a4b-b248-3fba4ab4aeb7 · outbound

This paper cites End-to-end object detection with transformers.

Extending Dataset Pruning to Object Detection: A Variance-based Approach End-to-end object detection with transformers

Reference 6

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Observation 7bed6439-3965-4ffa-bc9c-fed06ea61752 · outbound

This paper cites Dataset distillation by matching training trajectories.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Dataset distillation by matching training trajectories

Reference 7

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Observation f76b377f-f7c2-4770-a809-2f26945e7807 · outbound

This paper cites Generalizing dataset distillation via deep generative prior.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Generalizing dataset distillation via deep generative prior

Reference 8

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Observation 4117deb7-5a96-4850-9426-79ae900f8787 · outbound

This paper cites Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation

Reference 9

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Observation 58958b4e-c1ec-47d5-9215-fbeeec23e81b · outbound

This paper cites Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty

Reference 10

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Observation 225a397b-bbc6-4162-9fd6-9fa54193f89b · outbound

This paper cites Selection via Proxy: Efficient Data Selection for Deep Learning.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Selection via Proxy: Efficient Data Selection for Deep Learning

Reference 11

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Observation 6aba5ce3-d69e-4e97-b220-5ae635aebf1d · outbound

This paper cites Training-free dataset pruning for instance segmentation.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Training-free dataset pruning for instance segmentation

Reference 12

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Observation c9e48233-99d4-4bea-bc76-260db0d988b2 · outbound

This paper cites Minimizing the accumulated trajectory error to improve dataset distillation.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Minimizing the accumulated trajectory error to improve dataset distillation

Reference 13

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Observation 6567da2a-3145-4f74-a70f-b3cf94fcd5d3 · outbound

This paper cites Glam: Efficient scaling of language models with mixture-of-experts.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Glam: Efficient scaling of language models with mixture-of-experts

Reference 14

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Observation 52e8fa11-b979-40c8-a3d4-fc299d4b8121 · outbound

This paper cites The pascal visual object classes (voc) challenge.International journal of computer vision, 88: 303–338, 2010.

Extending Dataset Pruning to Object Detection: A Variance-based Approach The pascal visual object classes (voc) challenge.International journal of computer vision, 88: 303–338, 2010

Reference 15

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Observation 05fb2382-fd03-4951-8983-1053d30a5c3d · outbound

This paper cites Springer Science & Business Media, 2009.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Springer Science & Business Media, 2009

Reference 16

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Observation 3eccdba9-7c82-4419-9977-082be40e733d · outbound

This paper cites Fast r-cnn.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Fast r-cnn

Reference 17

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Observation 4fdc7ee7-3d97-4275-b534-2ffb0f211e99 · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Rich feature hierarchies for accurate object detection and semantic segmentation

Reference 18

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Observation f665c9dd-b98d-4a4e-ad53-e546596829b7 · outbound

This paper cites Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding

Reference 19

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Observation d1da7d60-6296-4d8d-8051-999f1a293c57 · outbound

This paper cites On coresets for k-means and k-median clustering.

Extending Dataset Pruning to Object Detection: A Variance-based Approach On coresets for k-means and k-median clustering

Reference 20

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Observation a2322ddf-2c47-4b68-899b-7729d209bb9b · outbound

This paper cites On coresets for k-means and k-median clustering.

Extending Dataset Pruning to Object Detection: A Variance-based Approach On coresets for k-means and k-median clustering

Reference 21

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Observation 4879c3e1-b67f-477c-a515-43e474a60ddc · outbound

This paper cites Deep residual learning for image recognition.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Deep residual learning for image recognition

Reference 22

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Observation d169eef4-8796-404e-aa46-dab25e120056 · outbound

This paper cites Girshick.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Girshick

Reference 23

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Observation e5d30dd7-b864-4ee8-aad5-e074170f01bd · outbound

This paper cites Large-scale dataset pruning with dynamic uncertainty.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Large-scale dataset pruning with dynamic uncertainty

Reference 24

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This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Extending Dataset Pruning to Object Detection: A Variance-based Approach MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 25

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This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 26

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Observation aa6b85f2-0c27-4c7e-9325-0015baf79075 · outbound

This paper cites LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices.

Extending Dataset Pruning to Object Detection: A Variance-based Approach LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices

Reference 27

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Observation e5ad811b-c75f-47c1-ad84-081b6434c688 · outbound

This paper cites Yolov5.https://github.com/ultralytics/yolov5, 2020.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Yolov5.https://github.com/ultralytics/yolov5, 2020

Reference 28

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Observation 8f829685-9560-488d-8729-6fb2f43579ee · outbound

This paper cites Parp: Prune, adjust and re-prune for self-supervised speech recognition.Advances in Neural Information Processing Systems, 34:21256–21272, 2021.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Parp: Prune, adjust and re-prune for self-supervised speech recognition.Advances in Neural Information Processing Systems, 34:21256–21272, 2021

Reference 29

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Observation 85b4148c-d35b-46f4-b56d-e27dd840c30a · outbound

This paper cites Coreset selection for object detection.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Coreset selection for object detection

Reference 30

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Observation e36c3780-b26d-4bf3-8674-b0ba969e300f · outbound

This paper cites Microsoft coco: Common objects in context.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Microsoft coco: Common objects in context

Reference 31

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Observation d262aba8-9170-4baa-997b-73104dbe821e · outbound

This paper cites Self-supervised learning for object detection: A survey.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Self-supervised learning for object detection: A survey

Reference 32

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Observation 785e8248-4f89-4744-9d00-ab3a68dd2c44 · outbound

This paper cites Ssd: Single shot multibox detector.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Ssd: Single shot multibox detector

Reference 33

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Observation 615a04ca-6212-4334-b3b0-6a020b346fe3 · outbound

This paper cites D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning.

Extending Dataset Pruning to Object Detection: A Variance-based Approach D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning

Reference 34

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This paper cites Deep learning on a data diet: Finding important examples early in training.Advances in neural information processing systems, 34:20596–20607, 2021.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Deep learning on a data diet: Finding important examples early in training.Advances in neural information processing systems, 34:20596–20607, 2021

Reference 35

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Observation c5b7af93-cc9b-4eb7-a62b-523189a9028c · outbound

This paper cites Identifying mislabeled data using the area under the margin ranking.Advances in Neural Information Processing Systems, 33:17044–17056, 2020.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Identifying mislabeled data using the area under the margin ranking.Advances in Neural Information Processing Systems, 33:17044–17056, 2020

Reference 36

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Observation a276dde9-46a9-47f4-9f73-9667ddb39b9d · outbound

This paper cites Fetch and forge: Efficient dataset condensation for object detection.Advances in Neural Information Processing Systems, 37:119283–119300, 2024.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Fetch and forge: Efficient dataset condensation for object detection.Advances in Neural Information Processing Systems, 37:119283–119300, 2024

Reference 37

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Observation 1b188692-8dfa-4bbb-8629-1e9ece4b32b1 · outbound

This paper cites YOLOv3: An Incremental Improvement.

Extending Dataset Pruning to Object Detection: A Variance-based Approach YOLOv3: An Incremental Improvement

Reference 38

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Observation 6d701ae7-6b35-41a0-921e-c353e0e3f2e6 · outbound

This paper cites You only look once: Unified, real-time object detection.

Extending Dataset Pruning to Object Detection: A Variance-based Approach You only look once: Unified, real-time object detection

Reference 39

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Observation 387d6f4f-63c3-414a-be28-354748bf0b35 · outbound

This paper cites Faster R-CNN: Towards real-time object detection with region proposal networks.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Faster R-CNN: Towards real-time object detection with region proposal networks

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Observation 5c931c22-1e57-4c2e-aba9-8f52bd56ec5d · outbound

This paper cites SVP-CF: Selection via Proxy for Collaborative Filtering Data.

Extending Dataset Pruning to Object Detection: A Variance-based Approach SVP-CF: Selection via Proxy for Collaborative Filtering Data

Reference 41

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Observation 68529803-2d23-4a55-95e6-fb307c68d0e9 · outbound

This paper cites Prototype selection for composite nearest neighbor classifiers.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Prototype selection for composite nearest neighbor classifiers

Reference 42

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Observation a38c9493-f49e-4228-a4b7-a8e16a1e7545 · outbound

This paper cites Beyond neural scaling laws: beating power law scaling via data pruning.Advances in Neural Information Processing Systems, 35:19523–19536, 2022.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Beyond neural scaling laws: beating power law scaling via data pruning.Advances in Neural Information Processing Systems, 35:19523–19536, 2022

Reference 43

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Observation 7c0b2fb9-3df6-4743-927e-2d3158ed35f6 · outbound

This paper cites Dˆ 4: Dataset distillation via disentangled diffusion model.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Dˆ 4: Dataset distillation via disentangled diffusion model

Reference 44

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Observation 48953249-84ad-410f-8889-bae3cfb414d5 · outbound

This paper cites Dataset cartography: Mapping and diagnosing datasets with training dynamics.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Dataset cartography: Mapping and diagnosing datasets with training dynamics

Reference 45

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Observation 68ff3960-f015-4f4e-9e19-c8ad4fad0e36 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:53:16.567624Z

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Observation 8490a478-2a56-4a2f-a771-5c09fb65d255 · outbound

This paper cites an unresolved cited work.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Unresolved cited work

Reference 47

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Observation dc509e37-b307-40a5-984e-b38540d85e99 · outbound

This paper cites Dataset Distillation.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Dataset Distillation

Reference 48

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Observation a600cf95-a831-449b-9c54-7e0dd832f7a9 · outbound

This paper cites $a^2$-DP: Annotation-aware data pruning for object detection, 2025.

Extending Dataset Pruning to Object Detection: A Variance-based Approach $a^2$-DP: Annotation-aware data pruning for object detection, 2025

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:53:16.290018Z

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Observation a424007a-885b-4b56-9ec3-b26d6cc475a2 · outbound

This paper cites Herding dynamical weights to learn.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Herding dynamical weights to learn

Reference 50

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Observation 04d3e082-2b66-4999-83f0-2e6a5569edeb · outbound

This paper cites CCNet: Extracting high quality monolingual datasets from web crawl data.

Extending Dataset Pruning to Object Detection: A Variance-based Approach CCNet: Extracting high quality monolingual datasets from web crawl data

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:53:16.024696Z

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Observation 86517642-b486-4d7e-9ee5-d949f43f02a5 · outbound

This paper cites Detectron2.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Detectron2

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:53:15.758527Z

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Observation 539a5386-b2e7-45dd-bc03-24cbbabf6c24 · outbound

This paper cites Moderate coreset: A universal method of data selection for real-world data-efficient deep learning.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Moderate coreset: A universal method of data selection for real-world data-efficient deep learning

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Observation 755af5c9-8674-4cd8-9635-22aaf29041b9 · outbound

This paper cites Are large-scale soft labels necessary for large-scale dataset distil- lation? InThe Thirty-eighth Annual Conference on Neural Information Processing Systems,.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Are large-scale soft labels necessary for large-scale dataset distil- lation? InThe Thirty-eighth Annual Conference on Neural Information Processing Systems,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:53:15.542820Z

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Observation 46e8f6a6-ff6f-45a6-8c39-e862e24231ea · outbound

This paper cites Dynamic data pruning for automatic speech recognition.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Dynamic data pruning for automatic speech recognition

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:53:15.090005Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 05e8812b-5747-4236-86f5-61e618f1b73f · outbound

This paper cites Data pruning can do more: A comprehensive data pruning approach for object re-identification.Transactions on Machine Learning Research, 2024.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Data pruning can do more: A comprehensive data pruning approach for object re-identification.Transactions on Machine Learning Research, 2024

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:53:14.939857Z

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Observation 975c77e7-f7fd-4f00-a627-c36228eb9f5d · outbound

This paper cites Dynamic Data Pruning for Automatic Speech Recognition.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Dynamic Data Pruning for Automatic Speech Recognition

Reference 57

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T14:53:13.879443Z

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Observation 19c7eccd-c80f-468b-8822-ec528f9c3abe · outbound

This paper cites an unresolved cited work.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:53:14.626973Z

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Observation 9750fd19-931f-4435-adce-de35df1439cb · outbound

This paper cites Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective.Advances in Neural Information Processing Systems, 36:73582–73603, 2023.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective.Advances in Neural Information Processing Systems, 36:73582–73603, 2023

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:53:14.763754Z

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Observation 9693edea-dc38-46bc-8e24-9566cd9aef86 · outbound

This paper cites Dataset condensation with differentiable siamese augmentation.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Dataset condensation with differentiable siamese augmentation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T14:53:13.321594Z

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Unavailable: canonical work link unavailable.

Observation daf4e9ff-bb47-4083-bebe-b6b7f3c41f0f · outbound

This paper cites Spanning training progress: Temporal dual-depth scoring (tdds) for enhanced dataset pruning.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Spanning training progress: Temporal dual-depth scoring (tdds) for enhanced dataset pruning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T14:53:13.190021Z

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Observation da884035-96d9-469c-a374-9389e8013c92 · outbound

This paper cites Coverage-centric Coreset Selection for High Pruning Rates.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Coverage-centric Coreset Selection for High Pruning Rates

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T14:53:13.532165Z

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Observation 4f99b91a-21d1-4e3c-bc78-0c8c0e4aa101 · outbound

This paper cites Boosting the Cross-Architecture Generalization of Dataset Distillation through an Empirical Study.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Boosting the Cross-Architecture Generalization of Dataset Distillation through an Empirical Study

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:53:13.758790Z

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Observation 6bf50210-0b1a-4d95-8ae5-18a31aaca62a · outbound

This paper cites Deformable detr: Deformable transformers for end-to-end object detection.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Deformable detr: Deformable transformers for end-to-end object detection

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:53:14.477161Z

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Observation ce1a66df-376c-4080-800d-4047aeac3e5a · outbound

This paper cites an unresolved cited work.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:53:15.252645Z

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