Paper Citation Record · LEDGER

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning

As of 24 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2412.16942.

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

pith.paper-citation-record.v1
2412.16942 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T06:01:43.959934Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

43 of 43 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 20e1d05f-b76d-4e1a-95e1-47ac7fd56815 · outbound

This paper cites Coreset sampling from open-set for fine-grained self-supervised learning,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Coreset sampling from open-set for fine-grained self-supervised learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.771804Z

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Observation f0681304-6aa6-42d5-adc4-8ea1025e5b60 · outbound

This paper cites Deep residual learning for image recognition,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Deep residual learning for image recognition,

Reference 2

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Observation 8c28085c-3827-4d39-8774-f6ed96c13bb4 · outbound

This paper cites Segnet: A deep convolutional encoder-decoder architecture for image segmentation,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Segnet: A deep convolutional encoder-decoder architecture for image segmentation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.740849Z

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Observation a0cb5930-03f3-4a8f-a8b6-85b2541f348c · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Fully convolutional networks for semantic segmentation,

Reference 4

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Observation 19b54398-8577-4cbe-a83f-2c7beafc0661 · outbound

This paper cites Photographic image synthesis with cascaded refinement networks,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Photographic image synthesis with cascaded refinement networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.704226Z

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Observation 099b0ae7-290b-4911-b8ae-edc82e602582 · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning High- resolution image synthesis with latent diffusion models,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.684055Z

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Observation f35a9759-ec99-47b1-b0e6-dafaffc97568 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Pointnet: Deep learning on point sets for 3d classification and segmentation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.664432Z

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Observation 93dcbdb5-c8be-4c69-9dd3-6112ff9b77a9 · outbound

This paper cites Stickypillars: Robust and efficient feature matching on point clouds using graph neural networks,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Stickypillars: Robust and efficient feature matching on point clouds using graph neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.646701Z

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Observation 6366106e-2beb-433c-9bb5-b5a89bd8bd5d · outbound

This paper cites Extreme consistency: Overcoming annotation scarcity and domain shifts,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Extreme consistency: Overcoming annotation scarcity and domain shifts,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.627993Z

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Observation c86b4ec9-3c73-475f-89de-d216d9073951 · outbound

This paper cites Self-supervised learning methods and applications in medical imaging analysis: A survey,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Self-supervised learning methods and applications in medical imaging analysis: A survey,

Reference 10

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Observation 2f2f4be9-e5e1-4a2d-9648-630514d0f7c0 · outbound

This paper cites Advances in deep learning models for resolving medical image segmentation data scarcity problem: A topical review,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Advances in deep learning models for resolving medical image segmentation data scarcity problem: A topical review,

Reference 11

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Observation feb365bc-9ee3-4bda-abca-9751886b60bf · outbound

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

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Imagenet: A large-scale hierarchical image database,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.580749Z

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Observation 9fa6aef6-82c9-4d1d-96ea-dcd0a15d8913 · outbound

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

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Bootstrap your own latent-a new approach to self-supervised learning,

Reference 13

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Observation a7f987d1-e2dc-445e-9eb9-b9f2d4db168d · outbound

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

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Emerging properties in self-supervised vision transformers,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.552607Z

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Observation 7dfd6249-3ec4-40d4-aad5-b4752a6b18aa · outbound

This paper cites Unsupervised Representation Learning by Predicting Image Rotations.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Unsupervised Representation Learning by Predicting Image Rotations

Reference 15

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Source-reported events for the cited work

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Observation 637dfd63-4752-4edf-ad13-da3b22781970 · outbound

This paper cites Colorful image colorization,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Colorful image colorization,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.534415Z

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Observation 2367fd4d-c20b-4bde-8adf-83d7f8b44689 · outbound

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

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning A simple framework for contrastive learning of visual representations,

Reference 17

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Observation 2da49e35-259f-4c38-a15f-00d87b7ce3b9 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Representation Learning with Contrastive Predictive Coding

Reference 18

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Observation 118a1c6e-17b7-4aa8-9761-5d763928d81d · outbound

This paper cites Self-supervised Pretraining of Visual Features in the Wild.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Self-supervised Pretraining of Visual Features in the Wild

Reference 19

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Observation f9ae763c-1da9-4bf8-a4cf-1353fa04ae5c · outbound

This paper cites How well do self- supervised models transfer?.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning How well do self- supervised models transfer?

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.503418Z

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Observation 3e9b313e-3a07-4250-9259-73b13dfaa23c · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 21

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Observation 8510a42d-3a1f-49ba-aee7-9f36366e0a19 · outbound

This paper cites Submodularity in data subset selection and active learning,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Submodularity in data subset selection and active learning,

Reference 22

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Observation 3cb45d45-cff5-42d3-a072-e75ce40bf494 · outbound

This paper cites Summary cache: a scalable wide-area web cache sharing protocol,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Summary cache: a scalable wide-area web cache sharing protocol,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.466901Z

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Observation f5938022-cd75-4d55-b512-fa7bd96fa4f9 · outbound

This paper cites Space/time trade-offs in hash coding with allowable errors,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Space/time trade-offs in hash coding with allowable errors,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.448797Z

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Observation 323de418-66c3-4d38-a22e-e356a61e6c48 · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Reproducible scaling laws for contrastive language-image learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.429877Z

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Observation d8d83c01-e73c-4594-aa9c-0d5a64930c21 · outbound

This paper cites LAION-5b: An open large- scale dataset for training next generation image-text models,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning LAION-5b: An open large- scale dataset for training next generation image-text models,

Reference 26

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Observation 937a7243-7156-4104-9a6f-d5e2ed358350 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Learning transferable visual models from natural language supervision,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.399126Z

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Observation a4ca195e-3182-460f-b306-f96fdceafbb5 · outbound

This paper cites Murmurhash3,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Murmurhash3,

Reference 28

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Observation 7920a590-401b-435d-8af0-99f18bf32dd6 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Fine-Grained Visual Classification of Aircraft

Reference 29

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Observation baab8c44-b0e1-4bba-9074-afd7b4bf7f94 · outbound

This paper cites 3d object representations for fine-grained categorization,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning 3d object representations for fine-grained categorization,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.359906Z

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Observation 40f429c1-418b-47ef-bee7-e1ae9813bd5f · outbound

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BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Cats and dogs,

Reference 31

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Observation 9b2222a9-1ad5-4245-a9a7-0c249f2f2bbd · outbound

This paper cites Caltech-UCSD Birds 200,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Caltech-UCSD Birds 200,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.328116Z

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Observation 54df246a-253c-47c9-b5a6-95b6b57a4153 · outbound

This paper cites Novel dataset for fine-grained image categorization: Stanford dogs,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Novel dataset for fine-grained image categorization: Stanford dogs,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.306113Z

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Observation e56e7a2f-e236-4014-b109-f86a957ef06e · outbound

This paper cites Automated flower classification over a large number of classes,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Automated flower classification over a large number of classes,

Reference 34

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Observation 5e03aee6-a377-453b-b76c-3241f587dc38 · outbound

This paper cites Human action recognition by learning bases of action attributes and parts,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Human action recognition by learning bases of action attributes and parts,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.276353Z

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Observation e3a083a7-5612-4219-9032-219638ff58c8 · outbound

This paper cites Recognizing indoor scenes,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Recognizing indoor scenes,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.258628Z

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Observation 542ce13e-25f7-4be0-955e-546ef5be4942 · outbound

This paper cites Describing textures in the wild,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Describing textures in the wild,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.238384Z

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Observation aa818929-207b-457a-b01b-7574fb72a6b8 · outbound

This paper cites Maskgan: Towards diverse and interactive facial image manipulation,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Maskgan: Towards diverse and interactive facial image manipulation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.219539Z

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Observation cc50af75-2edc-4a1a-889c-233b39de3f4d · outbound

This paper cites Food/non-food image classifi- cation and food categorization using pre-trained googlenet model,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Food/non-food image classifi- cation and food categorization using pre-trained googlenet model,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.196703Z

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Observation 726ae63f-f433-4662-91be-db3f40ccc7fb · outbound

This paper cites Microsoft coco: Common objects in context,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Microsoft coco: Common objects in context,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.176519Z

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Observation 6ec0dbf7-a619-49fa-9d0b-1beb315fd3c5 · outbound

This paper cites The inaturalist species classifica- tion and detection dataset,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning The inaturalist species classifica- tion and detection dataset,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.157053Z

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Observation 73491d29-0d74-48c7-9c4d-f7c15dacdba8 · outbound

This paper cites False negative problem of counting bloom filter,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning False negative problem of counting bloom filter,

Reference 42

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Observation a36ff9a6-f402-4b01-a4bd-ff179525aef4 · outbound

This paper cites Understanding contrastive representation learning through alignment and uniformity on the hypersphere,.

BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised Learning Understanding contrastive representation learning through alignment and uniformity on the hypersphere,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:01:44.127706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.