Paper Citation Record · LEDGER

On the Out-of-Distribution Generalization of Self-Supervised Learning

As of 9 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2505.16675.

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

pith.paper-citation-record.v1
2505.16675 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:03:25.019099Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

35 of 35 outbound references displayed

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External citation measurements

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Outbound references

Observation 818b06e1-791a-4bde-a0ab-5f3d8b0d3cca · outbound

This paper cites Reformulation of OOD Generalization as Generalization on Task Distributions.

On the Out-of-Distribution Generalization of Self-Supervised Learning Reformulation of OOD Generalization as Generalization on Task Distributions

Reference 3

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Observation a18a587d-b6d3-44ea-b9b5-77410cc5fb85 · outbound

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

On the Out-of-Distribution Generalization of Self-Supervised Learning An empirical study of training self-supervised vision transformers

Reference 5

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This paper cites Even though we train a V AE, it does not affect other components of SimCLR’s training pipeline, including the training objective, network architecture, and optimization algorithm.

On the Out-of-Distribution Generalization of Self-Supervised Learning Even though we train a V AE, it does not affect other components of SimCLR’s training pipeline, including the training objective, network architecture, and optimization algorithm

Reference 6

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This paper cites The Kinetics Human Action Video Dataset.

On the Out-of-Distribution Generalization of Self-Supervised Learning The Kinetics Human Action Video Dataset

Reference 8

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On the Out-of-Distribution Generalization of Self-Supervised Learning Unresolved cited work

Reference 10

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This paper cites URL http: //dx.doi.org/10.1109/iccv.2017.591.

On the Out-of-Distribution Generalization of Self-Supervised Learning URL http: //dx.doi.org/10.1109/iccv.2017.591

Reference 11

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This paper cites A., and Doretto, G.

On the Out-of-Distribution Generalization of Self-Supervised Learning A., and Doretto, G

Reference 12

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Observation 99d6a298-13d2-4531-bb8b-94976baac580 · outbound

This paper cites 2017.609.

On the Out-of-Distribution Generalization of Self-Supervised Learning 2017.609

Reference 13

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This paper cites Towards the Sparseness of Projection Head in Self-Supervised Learning.

On the Out-of-Distribution Generalization of Self-Supervised Learning Towards the Sparseness of Projection Head in Self-Supervised Learning

Reference 15

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Observation 7e806cdd-2fbb-45e9-99cf-8cef7c826c01 · outbound

This paper cites Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?.

On the Out-of-Distribution Generalization of Self-Supervised Learning Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?

Reference 17

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Observation 9fbaad86-65f9-42e8-8bfb-023c0d54e6e8 · outbound

This paper cites Hacking Task Confounder in Meta-Learning.

On the Out-of-Distribution Generalization of Self-Supervised Learning Hacking Task Confounder in Meta-Learning

Reference 18

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Observation be4d2515-c5dd-4c33-829a-174c94274764 · outbound

This paper cites Causal Balancing for Domain Generalization.

On the Out-of-Distribution Generalization of Self-Supervised Learning Causal Balancing for Domain Generalization

Reference 19

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Observation a4b98edb-903c-4b72-bdcc-3384601fab6d · outbound

This paper cites Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning.

On the Out-of-Distribution Generalization of Self-Supervised Learning Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning

Reference 20

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Observation 84059528-88cc-4e26-aaa1-cad02964f98b · outbound

This paper cites Barlow Twins: Self-Supervised Learning via Redundancy Reduction.

On the Out-of-Distribution Generalization of Self-Supervised Learning Barlow Twins: Self-Supervised Learning via Redundancy Reduction

Reference 21

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Observation b62ea652-71ef-45b7-b73c-17124cf4c648 · outbound

This paper cites Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling.

On the Out-of-Distribution Generalization of Self-Supervised Learning Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling

Reference 22

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Observation 1ac2a90a-f615-43cb-b6fb-3e6722030d3d · outbound

This paper cites Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement.

On the Out-of-Distribution Generalization of Self-Supervised Learning Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement

Reference 23

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Observation c6028c04-1fbf-469e-b37b-629e02d95cbd · outbound

This paper cites Since this holds for all x ∈ R and h ̸= 0, we conclude that the distribution is not strongly exponential.

On the Out-of-Distribution Generalization of Self-Supervised Learning Since this holds for all x ∈ R and h ̸= 0, we conclude that the distribution is not strongly exponential

Reference 24

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Observation cbf83745-3af4-4ac8-87f2-68d330c50169 · outbound

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On the Out-of-Distribution Generalization of Self-Supervised Learning Unresolved cited work

Reference 27

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Observation 0dc17672-c1c3-4a44-95a3-2a1c5ab4d9b6 · outbound

This paper cites The ViT backbone is adapted for use with FPN (Lin et al., 2017).

On the Out-of-Distribution Generalization of Self-Supervised Learning The ViT backbone is adapted for use with FPN (Lin et al., 2017)

Reference 29

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Observation 6d213c5e-0d66-49e2-bbcb-f3069eb5775a · outbound

This paper cites Following the standard setting, we evaluate on top 60 common classes with mean Average Precision (mAP) as the metric.

On the Out-of-Distribution Generalization of Self-Supervised Learning Following the standard setting, we evaluate on top 60 common classes with mean Average Precision (mAP) as the metric

Reference 30

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Observation b147348b-6e14-4d5a-9b41-0f18d16d062a · outbound

This paper cites During training, waterbirds (landbirds) are predominantly paired with water (land) backgrounds.

On the Out-of-Distribution Generalization of Self-Supervised Learning During training, waterbirds (landbirds) are predominantly paired with water (land) backgrounds

Reference 32

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Observation daf6e109-e5f4-42c7-98f7-0e6faa351be7 · outbound

This paper cites Finally, for the Colored-MNIST dataset, we follow the experimental settings mentioned in (Huang et al., 2024).

On the Out-of-Distribution Generalization of Self-Supervised Learning Finally, for the Colored-MNIST dataset, we follow the experimental settings mentioned in (Huang et al., 2024)

Reference 33

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Observation a75b1fdd-2381-4e7e-9971-aee834fa0f53 · outbound

This paper cites For this experiment, we utilize several benchmark datasets to evaluate the model’s performance.

On the Out-of-Distribution Generalization of Self-Supervised Learning For this experiment, we utilize several benchmark datasets to evaluate the model’s performance

Reference 128

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This paper cites URL http://dx.doi.org/10.21236/ada114514.

On the Out-of-Distribution Generalization of Self-Supervised Learning URL http://dx.doi.org/10.21236/ada114514

Reference 1981

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Observation 4b729abc-4b70-49ea-81ff-5c3fabff0ae7 · outbound

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

On the Out-of-Distribution Generalization of Self-Supervised Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2009

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This paper cites We also conduct experiments on a smaller version of Pascal VOC, the VOC 07 set (5K images), with a reduced number of iterations.

On the Out-of-Distribution Generalization of Self-Supervised Learning We also conduct experiments on a smaller version of Pascal VOC, the VOC 07 set (5K images), with a reduced number of iterations

Reference 2015

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Observation 3d5b8c4b-ca61-4953-84ab-66661c06e367 · outbound

This paper cites The following section provides a comparison of the models.

On the Out-of-Distribution Generalization of Self-Supervised Learning The following section provides a comparison of the models

Reference 2016

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Observation e28d6268-5848-4e2c-8332-0cadff9eb0d7 · outbound

This paper cites URL http://dx.

On the Out-of-Distribution Generalization of Self-Supervised Learning URL http://dx

Reference 2017

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Observation b420eecb-c581-4a41-9c26-6ec3bed7df84 · outbound

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

On the Out-of-Distribution Generalization of Self-Supervised Learning Bootstrap your own latent: A new approach to self-supervised Learning

Reference 2018

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This paper cites Auto-Encoding Variational Bayes.

On the Out-of-Distribution Generalization of Self-Supervised Learning Auto-Encoding Variational Bayes

Reference 2019

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Observation 9d1112ad-448c-4f5d-9cb6-a751de94a00c · outbound

This paper cites Unsupervised Learning of Visual Features by Contrasting Cluster Assignments.

On the Out-of-Distribution Generalization of Self-Supervised Learning Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Reference 2020

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On the Out-of-Distribution Generalization of Self-Supervised Learning Unresolved cited work

Reference 2021

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This paper cites Meta-learning with differentiable closed-form solvers.

On the Out-of-Distribution Generalization of Self-Supervised Learning Meta-learning with differentiable closed-form solvers

Reference 2022

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This paper cites On the Discriminability of Self-Supervised Representation Learning.

On the Out-of-Distribution Generalization of Self-Supervised Learning On the Discriminability of Self-Supervised Representation Learning

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:03:26.703868Z

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Observation 250bcebb-d883-433d-a753-ed3cb04ffbb5 · outbound

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On the Out-of-Distribution Generalization of Self-Supervised Learning Unresolved cited work

Reference 2024

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Pith citing papers

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