Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T14:22:24.594618Z
Paper Citation Record · LEDGER
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?
As of 10 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2501.15431.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
63 of 63 outbound references displayed
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External citation measurements
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Outbound references
Observation 15b4526a-9bce-4153-9cf2-79b78bb19fd9 · outbound
This paper cites ImageNet Classification with Deep Convolutional Neural Networks,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? ImageNet Classification with Deep Convolutional Neural Networks,
Reference 1
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This paper cites Very Deep Convolutional Networks For Large-Scale Image Recognition,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Very Deep Convolutional Networks For Large-Scale Image Recognition,
Reference 2
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This paper cites Aggregated Residual Transformations for Deep Neural Networks,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Aggregated Residual Transformations for Deep Neural Networks,
Reference 3
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Observation 75e8f294-47d7-462c-a489-6ef84b802de9 · outbound
This paper cites Data Labeling: An Empirical Investigation Into Industrial Challenges and Mitigation Strategies,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Data Labeling: An Empirical Investigation Into Industrial Challenges and Mitigation Strategies,
Reference 4
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Observation dead36ba-9b3b-442d-9798-f4e016a2ff4e · outbound
This paper cites Generalizing From a Few Examples: A Survey on Few-shot Learning,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Generalizing From a Few Examples: A Survey on Few-shot Learning,
Reference 5
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Observation ad95a6c8-1fe0-449a-bdb9-684dd051cf47 · outbound
This paper cites A Survey of Transfer Learning,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? A Survey of Transfer Learning,
Reference 6
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Observation db52ade0-afa7-44f1-a734-a92509ddf72c · outbound
This paper cites Automatic Differentiation in PyTorch,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Automatic Differentiation in PyTorch,
Reference 7
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Observation 619092d3-ec33-40b0-87d4-f672da856794 · outbound
This paper cites ImageNet Large Scale Visual Recognition Challenge,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? ImageNet Large Scale Visual Recognition Challenge,
Reference 8
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Source-reported events for the cited work
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Observation 4ae47e77-06f4-4d38-8a2c-bc148c755955 · outbound
This paper cites A Simple Frame- work for Contrastive Learning of Visual Representations,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? A Simple Frame- work for Contrastive Learning of Visual Representations,
Reference 9
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Source-reported events for the cited work
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Observation 3d9b58dd-9f8a-4ec9-9f4c-5c0acd17e0f1 · outbound
This paper cites Bootstrap your own latent-a new approach to self-supervised learning,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Bootstrap your own latent-a new approach to self-supervised learning,
Reference 10
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Source-reported events for the cited work
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Observation ed40e5c9-447a-4219-98d0-33f8c591af39 · outbound
This paper cites Momentum Contrast for Unsupervised Visual Representation Learning,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Momentum Contrast for Unsupervised Visual Representation Learning,
Reference 11
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Source-reported events for the cited work
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Observation f9e27c51-f766-4f70-b9a5-14b79b5f0ee8 · outbound
This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Reference 12
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Observation 16fd25ac-3233-49ee-a9da-ad173b60d3b9 · outbound
This paper cites Barlow Twins: Self-supervised Learning via Redundancy Reduction,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Barlow Twins: Self-supervised Learning via Redundancy Reduction,
Reference 13
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Source-reported events for the cited work
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Observation da9911df-b2ab-49d9-af8e-f196967b4e52 · outbound
This paper cites Prototypical Contrastive Learn- ing of Unsupervised Representations,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Prototypical Contrastive Learn- ing of Unsupervised Representations,
Reference 14
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Observation c5da504e-e401-4854-96e2-85b666cd0a67 · outbound
This paper cites Unsupervised learning of visual features by contrasting cluster assign- ments,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Unsupervised learning of visual features by contrasting cluster assign- ments,
Reference 15
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Observation ff23171b-3d3d-460d-9992-4f78cb7d245e · outbound
This paper cites Self-supervised Learning of Pretext- invariant Representations,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Self-supervised Learning of Pretext- invariant Representations,
Reference 16
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Observation c33a5f39-6444-48f2-9312-f09a1412c5e1 · outbound
This paper cites A Cookbook of Self-Supervised Learning.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? A Cookbook of Self-Supervised Learning
Reference 17
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Observation cc94b233-849b-45f7-bfd0-f514bdcf63ca · outbound
This paper cites Know Your Self- supervised Learning: A Survey on Image-based Generative and Discrim- inative Training,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Know Your Self- supervised Learning: A Survey on Image-based Generative and Discrim- inative Training,
Reference 18
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Source-reported events for the cited work
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Observation 9c6c4aa6-b20e-46f7-ae28-0e4e23fd26df · outbound
This paper cites A metric learning reality check,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? A metric learning reality check,
Reference 19
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Source-reported events for the cited work
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Observation 3ebc5f37-7080-40ef-80bf-494a754f1730 · outbound
This paper cites The Benchmark Lottery.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? The Benchmark Lottery
Reference 20
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This paper cites An Empirical Study of Training Self- supervised Vision Transformers,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? An Empirical Study of Training Self- supervised Vision Transformers,
Reference 21
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This paper cites Self-organizing neural network that discovers surfaces in random-dot stereograms,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Self-organizing neural network that discovers surfaces in random-dot stereograms,
Reference 22
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Observation f80feed7-643f-4fa3-a08d-66494922df42 · outbound
This paper cites Learning classification with unlabeled data,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Learning classification with unlabeled data,
Reference 23
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Colorful Image Colorization,
Reference 24
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Observation 08a35ef4-ccc4-4e6c-92ab-c1796a7ddad8 · outbound
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Learning Representations for Automatic Colorization,
Reference 25
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Photo-realistic single image super-resolution using a generative adversarial network,
Reference 26
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Context encoders: Feature learning by inpainting,
Reference 27
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Unsupervised Representation Learning by Predicting Image Rotations
Reference 28
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Unsupervised Visual Repre- sentation Learning by Context Prediction,
Reference 29
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Observation 6ae31582-72da-4e86-a2cc-e145ffd9b756 · outbound
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Split-brain Autoencoders: Unsu- pervised Learning by Cross-channel Prediction,
Reference 30
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Deep Clustering for Unsupervised Learning of Visual Features,
Reference 31
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Self-labelling via simultaneous clustering and representation learning
Reference 32
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Obow: Online bag-of-visual-words generation for self-supervised learn- ing,
Reference 33
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Exploring Simple Siamese Representation Learn- ing,
Reference 34
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Emerging properties in self-supervised vision transformers,
Reference 35
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Billion-scale similarity search with gpus,
Reference 36
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Observation 19c74b7f-331e-41fc-8685-e13cc2ca445b · outbound
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Representation Learning with Contrastive Predictive Coding
Reference 37
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Learning Representations by Predicting Bags of Visual Words,
Reference 38
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Observation aadc3aa0-81cc-4888-bd9c-d17f78926f5f · outbound
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Gradient-Based Learning Applied To Document Recognition,
Reference 39
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Microsoft Coco: Common Objects In Con- text,
Reference 40
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Observation 6e4aacdb-aeb4-4fb5-94d8-cd4ae8af75d8 · outbound
This paper cites A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
Reference 41
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Scaling and Benchmark- ing Self-supervised Visual Representation Learning,
Reference 42
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2af946fa-17fe-4d59-9d16-167639c75ddc · outbound
This paper cites Do Better Imagenet Models Transfer Better?,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Do Better Imagenet Models Transfer Better?,
Reference 43
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 49275b55-4149-4d90-a7d6-3e108adbaab4 · outbound
This paper cites How Well Do Self- supervised Models Transfer?,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? How Well Do Self- supervised Models Transfer?,
Reference 44
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a5b2af5a-c66b-4694-b996-8c791f715a76 · outbound
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Natural Adversarial Examples,
Reference 45
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 284a6010-79e4-4205-9e16-8fe42df9855a · outbound
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Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Learning Robust Global Representations by Penalizing Local Predictive Power,
Reference 46
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 90327375-6363-43b4-bf53-0e620397d9f6 · outbound
This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? The many faces of robustness: A critical analysis of out-of-distribution generalization,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:22:24.844540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a2828057-219a-4408-93ae-e136c0e6041e · outbound
This paper cites Improving Robustness Against Common Corruptions by Covariate Shift Adaptation,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Improving Robustness Against Common Corruptions by Covariate Shift Adaptation,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:22:24.835577Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3d2abb71-32a1-4644-acbd-0c48a883502a · outbound
This paper cites Do imagenet classifiers generalize to imagenet?,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Do imagenet classifiers generalize to imagenet?,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:22:24.826738Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 5ca3d186-de6e-4103-9f6f-2a2de5d21156 · outbound
This paper cites Impact of ImageNet Model Selection on Domain Adaptation,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Impact of ImageNet Model Selection on Domain Adaptation,
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:22:24.817030Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e9afa9a9-3160-4da2-9032-80561a8f2993 · outbound
This paper cites Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners
Reference 51
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T14:22:24.679843Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3da79000-24a1-4e57-8989-00d5a4202d23 · outbound
This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:22:24.556333Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2640b02a-4d70-49b0-9977-7f88757d6abf · outbound
This paper cites Contrastive Training for Improved Out-of-Distribution Detection.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Contrastive Training for Improved Out-of-Distribution Detection
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:22:24.560028Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 04db514c-afe5-41d8-bc98-8d0a787cf627 · outbound
This paper cites Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:22:24.563332Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 28391a1e-2d75-48fc-bee6-ab7d2cf5dade · outbound
This paper cites Deep Residual Learning For Image Recognition,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Deep Residual Learning For Image Recognition,
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:22:24.808515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c563558b-d8c7-4588-9eb8-ec3f1e1ec2f9 · outbound
This paper cites Going Deeper With Convolutions,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Going Deeper With Convolutions,
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:22:24.799731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b9a7c5a9-c0ea-4768-ba3d-1b75b5eceb1e · outbound
This paper cites Confident Learning: Estimating Uncertainty in Dataset Labels,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Confident Learning: Estimating Uncertainty in Dataset Labels,
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:22:24.790745Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b09017b6-7fb5-4221-8773-d985f19eac66 · outbound
This paper cites Selective brain damage: Measuring the disparate impact of model pruning,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Selective brain damage: Measuring the disparate impact of model pruning,
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:22:24.781572Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 14721a92-0cfb-40d9-bb65-71a8069a5139 · outbound
This paper cites Are we done with ImageNet?.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Are we done with ImageNet?
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:22:24.579251Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b26cf701-2921-4886-a62f-03e908f2c1de · outbound
This paper cites ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:22:24.584079Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 01b83b36-89fa-4b80-ad56-349491681eee · outbound
This paper cites Explaining and Harnessing Adversarial Examples,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Explaining and Harnessing Adversarial Examples,
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:22:24.770957Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b306310c-5f93-4cca-826a-a5b1d3f1cd9c · outbound
This paper cites Adversarial Examples In The Physical World,.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Adversarial Examples In The Physical World,
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:22:24.760425Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation eac66f4c-4d6a-4269-8828-65adc70e80eb · outbound
This paper cites Borenstein, L.
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Borenstein, L
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:22:24.749584Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Pith citing papers
No inbound Pith citation observations are available.