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Paper Citation Record · LEDGER

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?

As of 9 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 0 inbound Pith citation observations for arXiv:2607.13192.

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

pith.paper-citation-record.v1
2607.13192 v1

Coverage vector

measured 87 of 87 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T05:58:49.782338Z

measured 87 of 87 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

87 of 87 outbound references displayed

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Outbound references

Observation 4f04a85d-f721-4118-ab82-9cd701d6708e · outbound

This paper cites GPT-4 Technical Report.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? GPT-4 Technical Report

Reference 1

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Observation 5ec5b587-af49-4a0a-97c2-445190142c89 · outbound

This paper cites JGCL: Joint self-supervised and supervised graph contrastive learning.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? JGCL: Joint self-supervised and supervised graph contrastive learning

Reference 2

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Observation ad0fb807-3677-4f47-9815-824740b2d791 · outbound

This paper cites CrisisMMD: Multimodal twitter datasets from natural disasters.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? CrisisMMD: Multimodal twitter datasets from natural disasters

Reference 3

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Observation 1ab86eed-f9d0-4e4b-9a4d-66739e4dc899 · outbound

This paper cites Flamingo: A visual language model for few-shot learning.Advances in neural information processing systems, 35: 23716–23736, 2022.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Flamingo: A visual language model for few-shot learning.Advances in neural information processing systems, 35: 23716–23736, 2022

Reference 4

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Observation 73716a63-e318-4c77-9738-337d9c2020a5 · outbound

This paper cites Hyperspectral target detection using self-supervised background learning.Advances in Space Research, 74(2):628–646, 2024.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Hyperspectral target detection using self-supervised background learning.Advances in Space Research, 74(2):628–646, 2024

Reference 5

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Observation c085dd17-1f15-4749-bcc0-61541368d3e3 · outbound

This paper cites Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging.Nature Biomedical Engineering, 7(6):756–779, 2023.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging.Nature Biomedical Engineering, 7(6):756–779, 2023

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Observation eae15a13-93c9-45df-84d3-d2489047da45 · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? BEiT: BERT Pre-Training of Image Transformers

Reference 7

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Observation fc400826-bdad-426e-863a-519be6bc0539 · outbound

This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Reference 8

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Observation 75eb5ec4-e7f1-47d8-b1a7-62b7e2868c6b · outbound

This paper cites ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

Reference 9

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Observation ccbf8c50-6f53-4686-a02e-deedf42e885e · outbound

This paper cites Mixmatch: A holistic approach to semi-supervised learning.Advances in neural information processing systems, 32, 2019.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Mixmatch: A holistic approach to semi-supervised learning.Advances in neural information processing systems, 32, 2019

Reference 10

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Observation fdf3a6f2-ac4d-433e-88cc-07098f7c051d · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.Advances in neural information processing systems, 33:9912–9924, 2020.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Unsupervised learning of visual features by contrasting cluster assignments.Advances in neural information processing systems, 33:9912–9924, 2020

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Observation f3782da5-f387-43d2-a4e4-9d5df1bf7fb1 · outbound

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

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Emerging properties in self-supervised vision transformers

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Observation b3c2e38f-0926-44b9-8504-063d9d348a57 · outbound

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

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? A simple framework for contrastive learning of visual representations

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Observation e7d0fc89-8b13-4612-a58d-9e97e2f1636b · outbound

This paper cites Big self-supervised models are strong semi-supervised learners.Advances in neural information processing systems, 33:22243–22255, 2020.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Big self-supervised models are strong semi-supervised learners.Advances in neural information processing systems, 33:22243–22255, 2020

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Observation 39bc9793-986c-4f80-bd57-b36e8816963d · outbound

This paper cites Exploring simple siamese representation learning.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Exploring simple siamese representation learning

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Observation 1ffdaa9d-fa1d-41d8-a4c6-3f75ba96d54a · outbound

This paper cites Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis.Medical image analysis, 54:280–296, 2019.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis.Medical image analysis, 54:280–296, 2019

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Observation 16eea5d2-7d11-4e78-8fed-2dfdc8c3e357 · outbound

This paper cites Orienting novel 3D objects using self-supervised learning of rotation transforms.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Orienting novel 3D objects using self-supervised learning of rotation transforms

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Observation f9027b8b-d548-4e93-9521-6e7dc1360d41 · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? BERT: Pre-training of deep bidirectional transformers for language understanding

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Observation 04388052-84bc-43a1-95d6-fc3554ac23df · outbound

This paper cites Unsupervised visual representation learning by context prediction.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Unsupervised visual representation learning by context prediction

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Observation e59afc59-3600-4919-9a56-1ed362e9cc2f · outbound

This paper cites Self- supervised colorization towards monochrome-color camera systems using cycle cnn.IEEE Transactions on Image Processing, 30:6609–6622, 2021.

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Observation 5d3e5796-69d3-40fb-854a-5ed61420e185 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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Observation e4449698-704a-4d96-9589-27bc9b20d1af · outbound

This paper cites Everingham, L.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Everingham, L

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source=pdf_text observed=2026-08-02T05:58:42.605340Z digest=sha256:68a1ab63df87c1aedd7453690c46bb3d60a77dcc054089e04edf7ff0b85deff3

Observation 9884dc30-2d89-4b94-9b07-970dec39d438 · outbound

This paper cites The Pascal Visual Object Classes (VOC) Challenge.International Journal of Computer Vision, 88(2):303–338, 2010.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? The Pascal Visual Object Classes (VOC) Challenge.International Journal of Computer Vision, 88(2):303–338, 2010

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Observation 1ae20ef1-cea9-414a-8ce0-1da4aeed6d2a · outbound

This paper cites Self-supervised representation learning by rotation feature decoupling.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Self-supervised representation learning by rotation feature decoupling

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Observation 6d43ea98-2d20-45d7-969d-c88015334ff6 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020

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Observation 722ceb76-1875-48ed-9436-08b524ff9f8b · outbound

This paper cites A survey on self-supervised learning: Algorithms, applications, and future trends.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):9052–9071, 2024.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? A survey on self-supervised learning: Algorithms, applications, and future trends.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12):9052–9071, 2024

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Observation 22c12c24-a931-4640-8325-ad6a5babe12b · outbound

This paper cites CrisisKAN: Knowledge- infused and explainable multimodal attention network for crisis event classification.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? CrisisKAN: Knowledge- infused and explainable multimodal attention network for crisis event classification

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Observation 0fbb8446-71d9-402d-bed7-594812cccfb9 · outbound

This paper cites Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC).

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)

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Observation 699cf1fe-94a8-43b1-8843-91a44b3f6962 · outbound

This paper cites Deep residual learning for im- age recognition.

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Observation e14f0da7-79d8-4031-82b6-d9857ea74d56 · outbound

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Observation acbf5561-e899-4f46-ba53-fba6508b51dd · outbound

This paper cites Masked autoencoders are scalable vision learners.

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Observation 11d5e7cc-f570-4f4d-a47d-9148d18b2574 · outbound

This paper cites Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment.IEEE Transactions on Image Processing, 29:4041–4056, 2020.

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Observation d3c546e5-c28c-4ba4-be31-650ccb256255 · outbound

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Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Self-supervised learning by image colorization

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Observation 7dfe544f-02b6-43d4-a17b-62f2ab1f0c50 · outbound

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

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries

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Observation a2436833-be00-434e-90d3-98e7dd655ed0 · outbound

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Observation e89f65d5-88bf-4c27-b1d8-2365464f0253 · outbound

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Observation 32efa712-a4c4-4b65-a856-d30ea9d3e616 · outbound

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Observation 10ab5ff1-b0e6-4986-b309-3a37a9f25bd3 · outbound

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Observation 080963cc-3e33-4f17-bcc6-a4661fce2548 · outbound

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source=pdf_text observed=2026-08-02T05:58:47.997734Z digest=sha256:a61febc7d14d0cd913917cdec1fc9858107ef9147194496c262252d9f230eaad

Observation 8f7a6ad4-2e9b-473d-8791-d59d3c825227 · outbound

This paper cites Simmim: A simple framework for masked image modeling.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Simmim: A simple framework for masked image modeling

Reference 73

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source=pdf_text observed=2026-08-02T05:58:48.180701Z digest=sha256:78ed5a23276ee96986ac5869e2368952f00c3619e07cfff48e62ce893833bcb7

Observation 6414584d-b630-4a1d-98fc-cd07838aae9b · outbound

This paper cites Image enhanced rotation prediction for self-supervised learning.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Image enhanced rotation prediction for self-supervised learning

Reference 74

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source=pdf_text observed=2026-08-02T05:58:48.273513Z digest=sha256:7afb79a1434290866593bbf48d405ca4f42b41d61c88ebe8a6cca30b2fe698d2

Observation 0dbf9192-8061-4236-900e-04ea95d8fecf · outbound

This paper cites Self-supervised 3d action representation learning with skeleton cloud colorization.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(1):509–524, 2023.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Self-supervised 3d action representation learning with skeleton cloud colorization.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(1):509–524, 2023

Reference 75

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source=pdf_text observed=2026-08-02T05:58:48.348391Z digest=sha256:10f3883a5c7af8d5bbfbdff773f7c9db29f1dc0f1a245aa525b41ba24ba70023

Observation babb9b42-5912-4f8a-b345-19005b16bd1e · outbound

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

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Barlow twins: Self- supervised learning via redundancy reduction

Reference 76

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source=pdf_text observed=2026-08-02T05:58:48.480576Z digest=sha256:1952d06f6f637a89abcf9b3ad8febd84fd94f8f5e7f1379b772cd76622875c33

Observation a005fdfd-3faa-4adb-bd71-c8d867257f48 · outbound

This paper cites S4L: Self-supervised semi-supervised learning.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? S4L: Self-supervised semi-supervised learning

Reference 77

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source=pdf_text observed=2026-08-02T05:58:48.570716Z digest=sha256:eaadc659f1b1122e34ce835d1cf7c45ea9966315f7354282457209e76a4f1894

Observation 46752f6c-d181-48b6-8e5b-80ec5ffff29e · outbound

This paper cites Colorful image colorization.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Colorful image colorization

Reference 78

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source=pdf_text observed=2026-08-02T05:58:48.701453Z digest=sha256:989065eb587c6f0db531c0fd06e46aefd85163694bb26c542edd7b36c79b7ea1

Observation 6f453bf6-9e27-4425-ae54-b5a4ce7f37c9 · outbound

This paper cites Combining self-supervised and supervised learning with noisy labels.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Combining self-supervised and supervised learning with noisy labels

Reference 79

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source=pdf_text observed=2026-08-02T05:58:48.794228Z digest=sha256:36bb5b46e240549cc093f036b562bebec5554233d40ac9a61736844967590e91

Observation 289fe9be-bd3d-4dc9-be26-5ecf85ed6acf · outbound

This paper cites iBOT: Image BERT Pre-Training with Online Tokenizer.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 80

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source=pdf_text observed=2026-08-02T05:58:48.847518Z digest=sha256:4040b98ba22daf837bbd534b7be203f11f83e9b66598a716453b07efa370311b

Observation 9c21a95f-2874-4075-b6fa-8eb8c5ac42d7 · outbound

This paper cites 2|X∩Y| |X|+|Y| .(3).

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? 2|X∩Y| |X|+|Y| .(3)

Reference 81

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source=pdf_text observed=2026-08-02T05:58:49.004451Z digest=sha256:5a4cf537e5bf41a9d6949072f5d31e8b949671c9b2fb4011ac49a255c6df8df5

Observation e520be53-930b-4b45-b6c6-ddc8cf6de573 · outbound

This paper cites IoU, also called the Jaccard index, measures the average overlap between predicted masks and the ground truth.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? IoU, also called the Jaccard index, measures the average overlap between predicted masks and the ground truth

Reference 82

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source=pdf_text observed=2026-08-02T05:58:49.154602Z digest=sha256:8bddcfc0d3507206c8e4c6d07e9074a4abc737369443de96ea2e7713d652c1fc

Observation e91f8fbf-3ce5-44ab-b56a-7f1c80006c25 · outbound

This paper cites P= T P T P+F P.(5) 2.Recall:Also called sensitivity, this metric measures how many of the actual positive cases were correctly identified by the model.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? P= T P T P+F P.(5) 2.Recall:Also called sensitivity, this metric measures how many of the actual positive cases were correctly identified by the model

Reference 83

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Observation 0adb1e97-1854-4d5e-9e8a-cf823be1a113 · outbound

This paper cites It is computed as the mean of the Average Precision (AP) overNclasses: mAP= 1 N NX i=1 APi,(7) where APi denotes the AP for class i.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? It is computed as the mean of the Average Precision (AP) overNclasses: mAP= 1 N NX i=1 APi,(7) where APi denotes the AP for class i

Reference 84

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Observation 11652693-e6d4-4df2-9da9-cf9f9f464b16 · outbound

This paper cites SROCC= 1− 6 Pn i=1 d2 i n(n2 −1) ,(9) whered i is is the difference between the ranks ofy i andˆy.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? SROCC= 1− 6 Pn i=1 d2 i n(n2 −1) ,(9) whered i is is the difference between the ranks ofy i andˆy

Reference 85

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source=pdf_text observed=2026-08-02T05:58:49.470588Z digest=sha256:13c1fd1daa865e2dd985208462d88e7c48d60e35028dfc1049d7bd0bc12ce60f

Observation 8702adf4-eee4-4606-8874-a7b34ee089fc · outbound

This paper cites an unresolved cited work.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? Unresolved cited work

Reference 86

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source=pdf_text observed=2026-08-02T05:58:49.654052Z digest=sha256:2b076d62f47ff025f2bd5a436c2d5e2f39b24fbbee0de964cb954c87e508a8b5

Observation 43e70c49-9caf-4759-994a-9058b11df072 · outbound

This paper cites 22 Table 3: Classification performance of different SSL frameworks on CrisisMMD using 128 ×128 image inputs under the PFT and JT training paradigms with 100% labeled data.

Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training? 22 Table 3: Classification performance of different SSL frameworks on CrisisMMD using 128 ×128 image inputs under the PFT and JT training paradigms with 100% labeled data

Reference 87

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Pith citing papers

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