Paper Citation Record · LEDGER

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2505.04375.

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

pith.paper-citation-record.v1
2505.04375 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:34:35.364842Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

33 of 33 outbound references displayed

verified exact2
verified fuzzy16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1f4e102e-b132-402e-84fb-6be78b7d560f · outbound

This paper cites One-peace: Exploring one general representation model toward unlimited modalities, 2023.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise One-peace: Exploring one general representation model toward unlimited modalities, 2023

Reference 1

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Source-reported events for the cited work

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Observation 6f581d37-58a4-43ba-8e2c-8f5ce18e1b5b · outbound

This paper cites Omnivec: Learning robust representations with cross-modal sharing.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Omnivec: Learning robust representations with cross-modal sharing

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:35.825535Z

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Observation 9648d05a-bb6f-4566-bb91-628837c5777e · outbound

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Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Unresolved cited work

Reference 3

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This paper cites Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick

Reference 4

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Observation 5abf69b4-5866-4dae-ae4d-f6e44f65f494 · outbound

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Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Unresolved cited work

Reference 5

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Observation 0a9450fa-6492-47ae-9d7e-ff669e187b0f · outbound

This paper cites LeCun and Y.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise LeCun and Y

Reference 6

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Observation 82977dd0-253b-42f6-85e8-82e80ef303ef · outbound

This paper cites an unresolved cited work.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Unresolved cited work

Reference 7

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Observation 19caa90c-8d1d-4e22-bb32-5bc4a9692bb9 · outbound

This paper cites Cordeiro and G.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Cordeiro and G

Reference 8

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Observation 51bc5c31-f75e-4d79-b9c9-748640a438a3 · outbound

This paper cites Deep Active Learning in the Presence of Label Noise: A Survey.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Deep Active Learning in the Presence of Label Noise: A Survey

Reference 9

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Source-reported events for the cited work

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Observation 01f06668-54ba-4765-8823-dd97ca65788c · outbound

This paper cites On the interdependence between data selection and architecture optimization in deep active learning.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise On the interdependence between data selection and architecture optimization in deep active learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:35.730686Z

Source-reported events for the cited work

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Observation 3fa2609f-9eff-4538-a1a6-a53978695b39 · outbound

This paper cites An empirical study on the efficacy of deep active learning for image classification, 2022.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise An empirical study on the efficacy of deep active learning for image classification, 2022

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:34:35.715715Z

Source-reported events for the cited work

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Observation 362e6c7b-04c3-4f2f-aba6-8647374b76b9 · outbound

This paper cites Deep active learning: A reality check, 2024.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Deep active learning: A reality check, 2024

Reference 12

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Source-reported events for the cited work

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Observation 7c19990a-cb5e-4dda-b5ee-2033fc77df29 · outbound

This paper cites GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise

Reference 13

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Observation 9f4c02ab-270a-4448-be6d-4a45284388f6 · outbound

This paper cites Kolesnikov, A.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Kolesnikov, A

Reference 14

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Observation 5584cb34-e241-4078-9801-fe3a6924406d · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Swin transformer: Hierarchical vision transformer using shifted windows

Reference 15

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Observation bef62f4d-0199-4697-8620-166bd7f375f5 · outbound

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Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Unresolved cited work

Reference 16

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Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Unresolved cited work

Reference 17

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Observation 41665ca0-6242-4d28-8550-c9adc3189a33 · outbound

This paper cites Going deeper with convolutions.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Going deeper with convolutions

Reference 18

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This paper cites Deep residual learning for image recognition.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Deep residual learning for image recognition

Reference 19

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This paper cites Learning multiple layers of features from tiny images.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Learning multiple layers of features from tiny images

Reference 20

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

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Imagenet: A large-scale hierarchical image database

Reference 21

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Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Unresolved cited work

Reference 22

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Observation bb23b5f5-3c65-473d-8227-7ff03c89d3b7 · outbound

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

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise A simple framework for contrastive learning of visual representations

Reference 23

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This paper cites Momentum contrast for unsupervised visual representation learning.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Momentum contrast for unsupervised visual representation learning

Reference 24

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This paper cites Training data-efficient image transformers & distillation through attention.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Training data-efficient image transformers & distillation through attention

Reference 25

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This paper cites Visual Transformer for Task-aware Active Learning.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Visual Transformer for Task-aware Active Learning

Reference 26

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Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Kelei, G

Reference 27

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Observation 124db22b-418f-4d54-8bcd-3fcd8ca6f387 · outbound

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Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Rotman and R

Reference 28

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Observation 041f974a-074b-4193-bf1b-730450747daf · outbound

This paper cites Scaling vision transformers.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Scaling vision transformers

Reference 29

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Observation 83f020fa-66b7-48d5-8538-d1784e0da542 · outbound

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

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Emerging properties in self-supervised vision transformers

Reference 30

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This paper cites Crossvit: Cross-attention multi-scale vision transformer for image classification.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Crossvit: Cross-attention multi-scale vision transformer for image classification

Reference 31

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Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Transformers meet small datasets

Reference 32

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Observation a1c62524-f2a7-44ef-9083-d8f3d7f3842f · outbound

This paper cites Understanding Why ViT Trains Badly on Small Datasets: An Intuitive Perspective.

Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise Understanding Why ViT Trains Badly on Small Datasets: An Intuitive Perspective

Reference 33

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Pith citing papers

No inbound Pith citation observations are available.