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Paper Citation Record · LEDGER

Seeing the Whole in the Parts in Self-Supervised Representation Learning

As of 18 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 1 inbound Pith citation observation for arXiv:2501.02860.

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

pith.paper-citation-record.v1
2501.02860 v1

Coverage vector

measured 77 of 77 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:05:10.376190Z

measured 78 of 78 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:08:12.222057Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

77 of 77 outbound references displayed

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  • verified fuzzy49
  • unresolved28
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1965d9b5-3262-4ef4-95a9-3d4fd8b4c6a1 · outbound

This paper cites Self-supervised classification network.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Self-supervised classification network

Reference 1

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Observation fb996ee5-362f-47cf-a2e3-48cb3b81815e · outbound

This paper cites Unsupervised state representation learning in atari.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Unsupervised state representation learning in atari

Reference 2

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Observation 78607ab5-d11c-4828-8e36-6217ee87be0d · outbound

This paper cites Computing receptive fields of convolutional neural networks.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Computing receptive fields of convolutional neural networks

Reference 3

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Observation 549c1106-04e1-457c-b7b8-2b955696605b · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive architecture.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Self-supervised learning from images with a joint-embedding predictive architecture

Reference 4

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Observation acb9e82a-8985-4081-8d7a-531d17fbf004 · outbound

This paper cites Ernst, C \'e line Teuli \`e re, and Jochen Triesch.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Ernst, C \'e line Teuli \`e re, and Jochen Triesch

Reference 5

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Observation eeb04546-3e17-48c6-94bc-2fed247331ad · outbound

This paper cites Does the brain's ventral visual pathway compute object shape? Trends in Cognitive Sciences, 26 0 (12): 0 1119--1132, 2022.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Does the brain's ventral visual pathway compute object shape? Trends in Cognitive Sciences, 26 0 (12): 0 1119--1132, 2022

Reference 6

Resolution
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Observation 692821f4-42eb-48b7-b37e-fc67a2bbc704 · outbound

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

Seeing the Whole in the Parts in Self-Supervised Representation Learning Learning representations by maximizing mutual information across views

Reference 7

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Source-reported events for the cited work

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Observation d8414204-7363-4ce5-932d-53a4ece85d1c · outbound

This paper cites Deep learning models fail to capture the configural nature of human shape perception.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Deep learning models fail to capture the configural nature of human shape perception

Reference 8

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Observation a69bb0ec-e88b-4efe-8116-837aa24c2db8 · outbound

This paper cites Deep convolutional networks do not classify based on global object shape.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Deep convolutional networks do not classify based on global object shape

Reference 9

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Observation 6da08065-f07e-43bf-982b-11c1334e1627 · outbound

This paper cites Beit: Bert pre-training of image transformers.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Beit: Bert pre-training of image transformers

Reference 10

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Observation 9d708246-e376-4214-8ebf-9ad2e360df04 · outbound

This paper cites Vicreg: Variance-invariance-covariance regularization for self-supervised learning.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Vicreg: Variance-invariance-covariance regularization for self-supervised learning

Reference 11

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Observation 5ea2fea6-a17a-48e5-b3b3-01d0be0841b3 · outbound

This paper cites Vicregl: Self-supervised learning of local visual features.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Vicregl: Self-supervised learning of local visual features

Reference 12

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Observation 30d687b1-72da-4281-a8fc-bc718fbf4c67 · outbound

This paper cites Towards Democratizing Joint-Embedding Self-Supervised Learning.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Towards Democratizing Joint-Embedding Self-Supervised Learning

Reference 13

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Observation 7e79995d-76a9-470c-a514-9fcfb368f3d5 · outbound

This paper cites Approximating cnns with bag-of-local-features models works surprisingly well on imagenet.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Approximating cnns with bag-of-local-features models works surprisingly well on imagenet

Reference 14

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Observation 9f684a4e-0e2d-4beb-90ad-6deca81add2f · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Unsupervised learning of visual features by contrasting cluster assignments

Reference 15

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Observation 31ffa1a1-fd14-47ae-b3f8-07424c1cde2a · outbound

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

Seeing the Whole in the Parts in Self-Supervised Representation Learning Emerging properties in self-supervised vision transformers

Reference 16

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Observation 9a1937ff-7638-4d0c-a217-7bd46f40fdb5 · outbound

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

Seeing the Whole in the Parts in Self-Supervised Representation Learning A simple framework for contrastive learning of visual representations

Reference 17

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Observation 9dbc7b3b-3170-4746-98db-864ae2a58bb0 · outbound

This paper cites Exploring simple siamese representation learning.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Exploring simple siamese representation learning

Reference 18

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Observation 645bdb96-bb33-4857-b683-a63cbca5b65f · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Improved Baselines with Momentum Contrastive Learning

Reference 19

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Observation 172e0320-eeb8-487f-a30d-8da5e38ee1c5 · outbound

This paper cites An empirical study of training self-supervised vision transformers.

Seeing the Whole in the Parts in Self-Supervised Representation Learning An empirical study of training self-supervised vision transformers

Reference 20

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Observation 3fe4e582-b4d3-4797-8479-f9499da47ebb · outbound

This paper cites Intra-instance vicreg: Bag of self-supervised image patch embedding explains the performance.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Intra-instance vicreg: Bag of self-supervised image patch embedding explains the performance

Reference 21

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Observation 2ba0ac28-aec3-426b-88ae-7d78b7126e3e · outbound

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

Seeing the Whole in the Parts in Self-Supervised Representation Learning solo-learn: A library of self-supervised methods for visual representation learning

Reference 22

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Observation c82130c4-74c9-41ac-98e8-f0ea0f338506 · outbound

This paper cites Scaling up your kernels to 31x31: Revisiting large kernel design in cnns.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Scaling up your kernels to 31x31: Revisiting large kernel design in cnns

Reference 23

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Observation 14cfc647-d689-470c-a2a8-3da2f6a178b3 · outbound

This paper cites With a little help from my friends: Nearest-neighbor contrastive learning of visual representations.

Seeing the Whole in the Parts in Self-Supervised Representation Learning With a little help from my friends: Nearest-neighbor contrastive learning of visual representations

Reference 24

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Observation 69bdcc5c-b857-4b71-829f-4c642c690165 · outbound

This paper cites All4one: Symbiotic neighbour contrastive learning via self-attention and redundancy reduction.

Seeing the Whole in the Parts in Self-Supervised Representation Learning All4one: Symbiotic neighbour contrastive learning via self-attention and redundancy reduction

Reference 25

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This paper cites Unsupervised statistical learning of higher-order spatial structures from visual scenes.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Unsupervised statistical learning of higher-order spatial structures from visual scenes

Reference 26

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This paper cites Statistical learning of higher-order temporal structure from visual shape sequences.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Statistical learning of higher-order temporal structure from visual shape sequences

Reference 27

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Observation 3931129f-4a81-45b2-a89a-1aa6847669c8 · outbound

This paper cites Encoding multielement scenes: statistical learning of visual feature hierarchies.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Encoding multielement scenes: statistical learning of visual feature hierarchies

Reference 28

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This paper cites A Review on Discriminative Self-supervised Learning Methods in Computer Vision.

Seeing the Whole in the Parts in Self-Supervised Representation Learning A Review on Discriminative Self-supervised Learning Methods in Computer Vision

Reference 29

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Seeing the Whole in the Parts in Self-Supervised Representation Learning Learning representations by predicting bags of visual words

Reference 30

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This paper cites Bootstrap your own latent: A new approach to self-supervised learning.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Bootstrap your own latent: A new approach to self-supervised learning

Reference 31

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Seeing the Whole in the Parts in Self-Supervised Representation Learning Deep residual learning for image recognition

Reference 32

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Seeing the Whole in the Parts in Self-Supervised Representation Learning Momentum contrast for unsupervised visual representation learning

Reference 33

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Seeing the Whole in the Parts in Self-Supervised Representation Learning Masked autoencoders are scalable vision learners

Reference 34

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Observation ab950dba-dc1f-473c-ae3b-c233329fd044 · outbound

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Seeing the Whole in the Parts in Self-Supervised Representation Learning Benchmarking neural network robustness to common corruptions and perturbations

Reference 35

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Seeing the Whole in the Parts in Self-Supervised Representation Learning Data augmentation instead of explicit regularization

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.131545Z digest=sha256:9e36b376a6b7b56db05ca4c2ab3f281dcbc467f30a0ce5ed7465ead7a62c1e53

Observation 02141daa-ea7c-4583-ad52-688593b06a20 · outbound

This paper cites Learning deep representations by mutual information estimation and maximization.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Learning deep representations by mutual information estimation and maximization

Reference 37

Resolution
verified fuzzy
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Source-reported events for the cited work

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

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Observation c413c782-58a7-4865-b08b-44ad2b906c95 · outbound

This paper cites Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:11.475035Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.144773Z digest=sha256:d570e8baabed55dbd3d66abd6cf501a08571053bced79de6002dee820b2ab79a

Observation 9f849385-35ed-40a9-8c1a-7b99868fa076 · outbound

This paper cites Texture-like representation of objects in human visual cortex.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Texture-like representation of objects in human visual cortex

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:11.452946Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.151191Z digest=sha256:70f618c35896b606aeb63a422638b2b23ad47e6872192ac07f160c51827639d1

Observation 55398fc9-4f99-4d47-82f8-c0963fb2b293 · outbound

This paper cites Self-distilled self-supervised representation learning.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Self-distilled self-supervised representation learning

Reference 40

Resolution
verified fuzzy
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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.158682Z digest=sha256:144ae8a9750d6a04f73bbf7d1ad471e0ed652783e799c58ec78ca53b0319a2dd

Observation 61d0b929-131b-4477-8506-21e7d14c037e · outbound

This paper cites Shape-selective processing in deep networks: integrating the evidence on perceptual integration.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Shape-selective processing in deep networks: integrating the evidence on perceptual integration

Reference 41

Resolution
verified fuzzy
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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.164971Z digest=sha256:446efb8435eab16516462ca8ca9aaed2a240cbc4117c831b1e42c2020275d8d6

Observation 43098174-f47e-4fbe-84b8-311061184e38 · outbound

This paper cites Transformers in vision: A survey.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Transformers in vision: A survey

Reference 42

Resolution
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no resolver link, observed 2026-08-10T22:05:10.171856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.171856Z digest=sha256:eacfc17725a03906b237e51e6a3be619cd6b708818d6d5ecc7e225d3a872dc7c

Observation b40e32a0-f355-402a-88f7-36c814d0a1e6 · outbound

This paper cites Adversarial examples in the physical world.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Adversarial examples in the physical world

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:11.359577Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.177350Z digest=sha256:c9ae483200924efa4d0d445c17e5bc2cf371b66ec6ea8598bf74dcc102562071

Observation 680fef3a-9be3-4463-a385-b55259557f71 · outbound

This paper cites Are we ready for a new paradigm shift? a survey on visual deep mlp.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Are we ready for a new paradigm shift? a survey on visual deep mlp

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:11.323467Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.183485Z digest=sha256:d3f8f9bfcef6544023e7e92e4167672cc1bd24d5a45bbb40dd17f4ec8a572b38

Observation 577a239b-fb81-4a80-aaa9-cf676438f115 · outbound

This paper cites Self-supervised learning via maximum entropy coding.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Self-supervised learning via maximum entropy coding

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:11.298633Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.188753Z digest=sha256:1d4cd1c92a8c23414af5142ccdf469dbb2f69b4015fb6afdbefb99965c67d732

Observation 3c8e50d5-5fd0-4612-bdc4-cee6c59f4230 · outbound

This paper cites A convnet for the 2020s.

Seeing the Whole in the Parts in Self-Supervised Representation Learning A convnet for the 2020s

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:11.273446Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.194767Z digest=sha256:7c2b0be9bcb50244eab1f8c414f17ef52a328c1949842f55a96821ef48f381dd

Observation 6420d0dd-a1e2-49f0-9308-09eee96a6a68 · outbound

This paper cites Understanding the effective receptive field in deep convolutional neural networks.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Understanding the effective receptive field in deep convolutional neural networks

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-10T22:05:10.200686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.200686Z digest=sha256:8972b4cea3ad2a7d33e26811f577f9db005f68ee390113fdc01dba8f99adc351

Observation 78a35db2-984a-4768-81fa-92a9f815bc62 · outbound

This paper cites Deep reinforcement and infomax learning.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Deep reinforcement and infomax learning

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:11.223876Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.206806Z digest=sha256:da4ff63d49d45e3b41a7b80eb59551cf4641e05866d6c41498de66fe8c47bab3

Observation db817367-b9c5-419e-8ecc-e2cc10a201dc · outbound

This paper cites Unsupervised learning of visual representations by solving jigsaw puzzles.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Unsupervised learning of visual representations by solving jigsaw puzzles

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:11.203197Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.213159Z digest=sha256:b3fc55bf78f8024d29fbcde416b27b692629a932a63267d73d1b418af61df2af

Observation a9a80d98-97e7-4960-a857-e548c517f086 · outbound

This paper cites Self-supervised learning with an information maximization criterion.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Self-supervised learning with an information maximization criterion

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-10T22:05:10.218532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.218532Z digest=sha256:9a7655feabf17a43818d99b8b0bfe8b84fe177878be384025aa1af2b1e36dc0a

Observation 9343aa88-dc3d-4580-88f2-211a7cfb610d · outbound

This paper cites On the integration of self-attention and convolution.

Seeing the Whole in the Parts in Self-Supervised Representation Learning On the integration of self-attention and convolution

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:11.153141Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.223795Z digest=sha256:094538d4109a44c29d6876cbf7829069c87a00781044c865ce95c8fde26fab2e

Observation 814aed8c-c04c-4a8f-9236-c3293814b6c1 · outbound

This paper cites Unsupervised visual representation learning by synchronous momentum grouping.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Unsupervised visual representation learning by synchronous momentum grouping

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:11.123884Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.232524Z digest=sha256:8dc91c0a9b87f5befabff45783208791d306fef4c21cc9018aa73dad0257b8d7

Observation 5878af0f-2b20-493d-97e2-76a3f085f83e · outbound

This paper cites Self-supervised video pretraining yields robust and more human-aligned visual representations.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Self-supervised video pretraining yields robust and more human-aligned visual representations

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:11.094278Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.237891Z digest=sha256:2c48bd1f29eedebac07ed5c3fc666f78cb9c7378813280c00922414a98835449

Observation c7b35d05-7450-42bc-938c-726ff5a330ce · outbound

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

Seeing the Whole in the Parts in Self-Supervised Representation Learning Learning transferable visual models from natural language supervision

Reference 54

Resolution
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no resolver link, observed 2026-08-10T22:05:10.244440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.244440Z digest=sha256:b37c29770e160c6abe15a8e7a9cc4306df0912a27394b760e2050c8ae29f7eff

Observation f5296fb4-c7c9-4c04-8665-95587bac5b23 · outbound

This paper cites Foolbox: A Python toolbox to benchmark the robustness of machine learning models.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Foolbox: A Python toolbox to benchmark the robustness of machine learning models

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-10T22:05:10.250890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.250890Z digest=sha256:889ba35263a6ccc365dcda34e16cb73a324981f4208e0748c8f976e0724af95c

Observation fde734bd-3f8a-437e-a5a7-30561329407c · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-10T22:05:10.257893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.257893Z digest=sha256:8d72ac3dca90b32ab891f2817d135201b57886c491d64ed80b41a1322cc3db49

Observation 9ca81cf1-669e-4c07-ac3e-6af7332af573 · outbound

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

Seeing the Whole in the Parts in Self-Supervised Representation Learning Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:11.025415Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.264054Z digest=sha256:5cc24d6065daf32b32f823c78e74d40bc6125159de41372b945e7e2017d19b36

Observation 9e24b10d-19c2-4cea-a3ec-885801b86d71 · outbound

This paper cites Understanding self-supervised learning dynamics without contrastive pairs.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Understanding self-supervised learning dynamics without contrastive pairs

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.998699Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.270438Z digest=sha256:a22431730790d79b172bee125f49d0ac756d8994b32b3045feb69b60d4b14179

Observation d7365d75-7121-408a-a516-516bb3e13829 · outbound

This paper cites EMP-SSL: Towards Self-Supervised Learning in One Training Epoch.

Seeing the Whole in the Parts in Self-Supervised Representation Learning EMP-SSL: Towards Self-Supervised Learning in One Training Epoch

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-10T22:05:10.278485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.278485Z digest=sha256:1f672ca7ffc727ad1d905b5bdf50e0a019bfabed83a302891ffa22e694459733

Observation e892d56c-0ed2-406e-9ce9-19c39f77ec59 · outbound

This paper cites Patches are all you need? Transactions on Machine Learning Research, 2023.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Patches are all you need? Transactions on Machine Learning Research, 2023

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.969767Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.284177Z digest=sha256:dbfa286fcc591a29091d19fad63edad55c1306661f8881c3fdb2a6d187d135e2

Observation 743e971a-cb86-412a-aed0-348397fcd76f · outbound

This paper cites The automaticity of visual statistical learning.

Seeing the Whole in the Parts in Self-Supervised Representation Learning The automaticity of visual statistical learning

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.946409Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.288732Z digest=sha256:07805322462c1937b8bb99429cbd4ced78ebe06f5cf83f8d3ba1156b8e05489a

Observation ae13ac6f-6b57-4523-b6a7-62bb912d89a6 · outbound

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

Seeing the Whole in the Parts in Self-Supervised Representation Learning Self-supervised learning by estimating twin class distribution

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.918189Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.293067Z digest=sha256:c37688e52fb8eb7c2e16782eb10d8244c56cbe71f46a8fd1b5b2b0b3cea783cd

Observation d4ee1229-fdd3-436d-9b67-ea5e177c9b0d · outbound

This paper cites Pose-aware self-supervised learning with viewpoint trajectory regularization.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Pose-aware self-supervised learning with viewpoint trajectory regularization

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.893939Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.297577Z digest=sha256:1cd6262ee7911cc199deeb6a42c394b844a4c98ffcff508a12e06196987952f1

Observation 7cc5d9ca-b596-49c1-a2c5-56fa30262f12 · outbound

This paper cites Contrastive learning with stronger augmentations.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Contrastive learning with stronger augmentations

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.875128Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.302798Z digest=sha256:dc798c6788ce2ced099c3e5307827e92c0b2584279927c57c8ba47e84a423678

Observation 2d326493-4ad6-429c-9cc2-cfbe9ec9557b · outbound

This paper cites Caco: Both positive and negative samples are directly learnable via cooperative-adversarial contrastive learning.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Caco: Both positive and negative samples are directly learnable via cooperative-adversarial contrastive learning

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.849822Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.308070Z digest=sha256:566f9ea348761f18184096bec9d553dacf2a4dbe69da0c1dc780135c5e183e27

Observation 5f6029ed-715a-4120-a7e6-1127cce260c6 · outbound

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

Seeing the Whole in the Parts in Self-Supervised Representation Learning Dense contrastive learning for self-supervised visual pre-training

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.827201Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.313184Z digest=sha256:42bd3dbe627d265e3cf9ee775f86d7eb44d679b7787206694db595ac0fe3c26c

Observation f006865a-b60f-48a3-8c48-be90dc0b6c21 · outbound

This paper cites Detco: Unsupervised contrastive learning for object detection.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Detco: Unsupervised contrastive learning for object detection

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.800361Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.317677Z digest=sha256:dcf6aca3d971d8156055de6023cf58f71b4e0654a6a13c12063b161358a8a660

Observation baae4dd8-aba6-4cf9-9e9f-1482e7dbbe27 · outbound

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

Seeing the Whole in the Parts in Self-Supervised Representation Learning Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.773155Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.324072Z digest=sha256:85df320c2ca7dc1a4471eb44809a5998b0c01a19273b50182eb9dc05c197d8e0

Observation c1da68de-8228-4422-a759-ae50d9f4d4f7 · outbound

This paper cites Patch-level representation learning for self-supervised vision transformers.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Patch-level representation learning for self-supervised vision transformers

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.755836Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.329544Z digest=sha256:7ce88985806cb7a0afd40feceb70c9b3a3165ea93397de384494fb7b7c73db82

Observation 33cc328e-429d-4974-9b0f-716ab51f1600 · outbound

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

Seeing the Whole in the Parts in Self-Supervised Representation Learning Barlow twins: Self-supervised learning via redundancy reduction

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-10T22:05:10.334280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.334280Z digest=sha256:61eb7cf23f138c84a1f9e65770b631b5939002d46e2ad292fab681304f4f1850

Observation 74353c0e-8ee6-455a-b555-e9133383f377 · outbound

This paper cites Matrix information theory for self-supervised learning.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Matrix information theory for self-supervised learning

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.719586Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-10T22:05:10.339158Z digest=sha256:e6c547c1953460bf25cd2b89ccd6e3a85dbc8af5c44d39e5add68c8510297023

Observation 7e1d2a00-a8e1-489f-b6f5-7a5e399c3c96 · outbound

This paper cites Places: A 10 million image database for scene recognition.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Places: A 10 million image database for scene recognition

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-10T22:05:10.344443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.344443Z digest=sha256:70c87b909eb81f22cddd3e222a7e0096ff620d9a5ab80cd9f521360eed9bb15b

Observation 2d1bf3e5-87a1-4bba-b878-d3d67094b438 · outbound

This paper cites Self-supervised learning of object parts for semantic segmentation.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Self-supervised learning of object parts for semantic segmentation

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:05:10.675508Z

Source-reported events for the cited work

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

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Observation bd10a7ce-5c17-408f-8b2f-2723c025c8e8 · outbound

This paper cites write newline.

Seeing the Whole in the Parts in Self-Supervised Representation Learning write newline

Reference 74

Resolution
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no resolver link, observed 2026-08-10T22:05:10.354775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.354775Z digest=sha256:d785146edb8701aaf7b55d641e73b608f0615b655c7215663b9a469cd24fca7f

Observation 21c7602c-745c-4cef-ba17-38fe222bc1f9 · outbound

This paper cites @esa (Ref.

Seeing the Whole in the Parts in Self-Supervised Representation Learning @esa (Ref

Reference 75

Resolution
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no resolver link, observed 2026-08-10T22:05:10.360946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.360946Z digest=sha256:1c6f580b3f933983f9565bce2eed182e27bf90139ff6553e596f500fc26d9256

Observation a9812bec-8698-400c-a0d9-245a99b03932 · outbound

This paper cites an unresolved cited work.

Seeing the Whole in the Parts in Self-Supervised Representation Learning Unresolved cited work

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-10T22:05:10.368863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.368863Z digest=sha256:6b2f0dcd5c4b17503556842f69cbf7e5862e7a56b4253ad66494789c80b1ccb9

Observation 9c18a64b-203c-480e-ae54-186fba622785 · outbound

This paper cites A survey on intrinsic motivation in reinforcement learning.

Seeing the Whole in the Parts in Self-Supervised Representation Learning A survey on intrinsic motivation in reinforcement learning

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-10T22:05:10.376190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:05:10.376190Z digest=sha256:641be7ad1642d0f5a64b293af89b2ef0cd16c194e6b3e0c8488bc0ba4286cd5b

Pith citing papers

Observation cd0f9334-989b-47ef-86ef-13b5ca8fa5aa · inbound

Self-Supervised Learning with a Multi-Task Latent Space Objective cites this paper.

Self-Supervised Learning with a Multi-Task Latent Space Objective Seeing the Whole in the Parts in Self-Supervised Representation Learning

Reference 3

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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