Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution | Paper Citation Record

Observation 985c4449-2714-4ade-83b2-f8dcc282e93a · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Dataset and study.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Ntire 2017 challenge on single image super-resolution: Dataset and study

Reference 1

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Observation 8cd266de-dfed-41b7-9cae-7e4aee3ecfd1 · outbound

This paper cites Optimization of imaging reconnaissance systems using super- resolution: Efficiency analysis in interference conditions.Sensors (Basel, Switzerland), 24(24):7977, 2024.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Optimization of imaging reconnaissance systems using super- resolution: Efficiency analysis in interference conditions.Sensors (Basel, Switzerland), 24(24):7977, 2024

Reference 2

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Observation 72ca3f4e-9370-4358-8502-64ec5658d0a1 · outbound

This paper cites Toward real-world single image super-resolution: A new benchmark and a new model.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Toward real-world single image super-resolution: A new benchmark and a new model

Reference 3

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Observation 97028fc2-ae36-4c45-ab45-0e37e8336b4f · outbound

This paper cites Adversarial diffusion compression for real-world image super-resolution.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Adversarial diffusion compression for real-world image super-resolution

Reference 4

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Observation 4d1d8a93-662a-40de-bed5-d048e679e9cf · outbound

This paper cites Hat: Hybrid attention transformer for image restoration.arXiv preprint arXiv:2309.05239, 2023.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Hat: Hybrid attention transformer for image restoration.arXiv preprint arXiv:2309.05239, 2023

Reference 5

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Observation 69aabfb2-0dae-4749-9169-c7b712d973e3 · outbound

This paper cites Multinet++: Multi-stream feature aggregation and geometric loss strategy for multi-task learning.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Multinet++: Multi-stream feature aggregation and geometric loss strategy for multi-task learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.814484Z

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Observation 7e50f9d6-6482-47e8-9ea8-4558613cd23b · outbound

This paper cites Learning a deep convolutional network for image super-resolution.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Learning a deep convolutional network for image super-resolution

Reference 7

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Observation c6ef3efc-45b9-4686-8dbb-cc07e8583cfb · outbound

This paper cites Tsd-sr: One-step diffusion with target score distillation for real-world image super- resolution.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Tsd-sr: One-step diffusion with target score distillation for real-world image super- resolution

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.776173Z

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Observation 95b721d3-05f8-41f4-bed0-9d8cf3ada094 · outbound

This paper cites Adadiffsr: Adaptive region-aware dynamic acceleration diffusion model for real-world image super-resolution.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Adadiffsr: Adaptive region-aware dynamic acceleration diffusion model for real-world image super-resolution

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.741152Z

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Observation 0763223b-6184-4022-ad1d-817c9bfda5a2 · outbound

This paper cites Efficiently identifying task groupings for multi-task learning.Advances in Neural Information Processing Systems, 34:27503–27516, 2021.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Efficiently identifying task groupings for multi-task learning.Advances in Neural Information Processing Systems, 34:27503–27516, 2021

Reference 10

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Observation a9331c21-ec23-4134-8342-ef659f4d1638 · outbound

This paper cites Sample-level weighting for multi-task learning with auxiliary tasks.Applied Intelligence, 54(4):3482–3501, 2024.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Sample-level weighting for multi-task learning with auxiliary tasks.Applied Intelligence, 54(4):3482–3501, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.703402Z

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Observation e89e65c7-3047-485a-9829-22cd78adf44b · outbound

This paper cites Blind super-resolution with iterative kernel correction.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Blind super-resolution with iterative kernel correction

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.679122Z

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Observation 641e43b4-9a64-48b7-8d3a-0165aa3bcab6 · outbound

This paper cites Dynamic task prioritization for multitask learning.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Dynamic task prioritization for multitask learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.652727Z

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Observation 75eaccf0-d4e4-4c07-99cb-7a55e4dc6c0b · outbound

This paper cites Unfolding the alternating optimization for blind super resolution.Advances in Neural Information Processing Systems, 33:5632–5643, 2020.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Unfolding the alternating optimization for blind super resolution.Advances in Neural Information Processing Systems, 33:5632–5643, 2020

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.633611Z

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Observation 763b9361-e093-4b16-854c-4755851d6db6 · outbound

This paper cites Multi-task learning using uncertainty to weigh losses for scene geometry and semantics.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Multi-task learning using uncertainty to weigh losses for scene geometry and semantics

Reference 15

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Observation 1c4b26e3-fdc0-42dd-9288-208140b498ea · outbound

This paper cites Accurate image super-resolution using very deep convolutional networks.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Accurate image super-resolution using very deep convolutional networks

Reference 16

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Observation 03017ab4-8db3-4c63-ab09-6d48277d2448 · outbound

This paper cites Ubernet: Training a universal convolutional neural network for low-, mid-, and high- level vision using diverse datasets and limited memory.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Ubernet: Training a universal convolutional neural network for low-, mid-, and high- level vision using diverse datasets and limited memory

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.570458Z

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Observation dee022d1-797f-40b0-9d38-04a54487f081 · outbound

This paper cites Photo-realistic single image super- resolution using a generative adversarial network.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Photo-realistic single image super- resolution using a generative adversarial network

Reference 18

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Observation 9d59b9ac-ac6c-4a3c-a3ce-c9535f6b2497 · outbound

This paper cites Efficient and degradation-adaptive network for real-world image super-resolution.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Efficient and degradation-adaptive network for real-world image super-resolution

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.532579Z

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Observation dcc0aa80-74f7-40c2-a040-64ba0ea6493c · outbound

This paper cites Swinir: Image restoration using swin transformer.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Swinir: Image restoration using swin transformer

Reference 20

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This paper cites an unresolved cited work.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Unresolved cited work

Reference 21

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Observation 8eb7ccda-32aa-4ce8-a931-5fbd297f81a9 · outbound

This paper cites Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning

Reference 22

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This paper cites End-to-end multi-task learning with attention.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution End-to-end multi-task learning with attention

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.466651Z

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This paper cites Cross-stitch networks for multi- task learning.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Cross-stitch networks for multi- task learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.440592Z

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Observation 8d0e413e-8a51-4121-8e66-4ccf624965e9 · outbound

This paper cites Metric learning based interactive modulation for real-world super-resolution.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Metric learning based interactive modulation for real-world super-resolution

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.419755Z

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Observation 429a5092-15c6-4610-94fe-e31fb8bd7668 · outbound

This paper cites Deep learning based autonomous vehicle super resolution doa estimation for safety driving.IEEE Transactions on Intelligent Transportation Systems, 22(7):4301–4315, 2020.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Deep learning based autonomous vehicle super resolution doa estimation for safety driving.IEEE Transactions on Intelligent Transportation Systems, 22(7):4301–4315, 2020

Reference 26

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This paper cites Vcisr: Blind single image super-resolution with video compression synthetic data.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Vcisr: Blind single image super-resolution with video compression synthetic data

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.347688Z

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Observation 7e14360a-763d-4b13-a7b3-450fc41c7be9 · outbound

This paper cites Unsupervised degradation representation learning for blind super-resolution.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Unsupervised degradation representation learning for blind super-resolution

Reference 28

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Observation 801e6a2a-fe9c-4ef7-bcce-e67f265d461c · outbound

This paper cites Real-esrgan: Training real-world blind super- resolution with pure synthetic data.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Real-esrgan: Training real-world blind super- resolution with pure synthetic data

Reference 29

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Observation 56f7b989-4744-40d1-8012-b2b489e27d14 · outbound

This paper cites Recovering realistic texture in image super- resolution by deep spatial feature transform.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Recovering realistic texture in image super- resolution by deep spatial feature transform

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.224737Z

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This paper cites Sinsr: diffusion-based image super-resolution in a single step.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Sinsr: diffusion-based image super-resolution in a single step

Reference 31

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Observation 5544ac63-f7cb-40e7-b95d-c828f4df22ec · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004

Reference 32

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Observation 90d55c2a-dd52-4c82-8851-a23b107045a7 · outbound

This paper cites Component divide-and-conquer for real-world image super-resolution.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Component divide-and-conquer for real-world image super-resolution

Reference 33

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Observation 520b53c6-8d2a-48c0-a671-c2f4969a53b9 · outbound

This paper cites One-step effective diffusion network for real-world image super-resolution.Advances in Neural Information Processing Systems, 37:92529–92553, 2024.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution One-step effective diffusion network for real-world image super-resolution.Advances in Neural Information Processing Systems, 37:92529–92553, 2024

Reference 34

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This paper cites Seesr: Towards semantics-aware real-world image super-resolution.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Seesr: Towards semantics-aware real-world image super-resolution

Reference 35

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This paper cites Gradient surgery for multi-task learning.Advances in neural information processing systems, 33:5824–5836, 2020.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Gradient surgery for multi-task learning.Advances in neural information processing systems, 33:5824–5836, 2020

Reference 36

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Observation c789def9-de8b-4957-abda-4ed3b2d04345 · outbound

This paper cites Resshift: Efficient diffusion model for image super- resolution by residual shifting.Advances in Neural Information Processing Systems, 36:13294–13307, 2023.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Resshift: Efficient diffusion model for image super- resolution by residual shifting.Advances in Neural Information Processing Systems, 36:13294–13307, 2023

Reference 37

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This paper cites Achievement-based training progress balancing for multi-task learning.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Achievement-based training progress balancing for multi-task learning

Reference 38

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This paper cites Taskonomy: Disentangling task transfer learning.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Taskonomy: Disentangling task transfer learning

Reference 39

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Observation 72c3b45d-9ca7-4338-bd59-e52e42970a38 · outbound

This paper cites Designing a practical degradation model for deep blind image super-resolution.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Designing a practical degradation model for deep blind image super-resolution

Reference 40

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This paper cites Learning a single convolutional super-resolution network for multiple degradations.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Learning a single convolutional super-resolution network for multiple degradations

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:17.028115Z

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Observation ac2db0a4-680d-48e7-a4f2-86dcb06f3645 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution The unreasonable effectiveness of deep features as a perceptual metric

Reference 42

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Observation a65b21c2-a737-492d-b738-640301f6a8d3 · outbound

This paper cites Real-world image super-resolution as multi-task learning.Advances in Neural Information Processing Systems, 36:21003–21022, 2023.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Real-world image super-resolution as multi-task learning.Advances in Neural Information Processing Systems, 36:21003–21022, 2023

Reference 43

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Observation d15c356b-74e6-4e8d-873e-58a593fcab7c · outbound

This paper cites Image super-resolution using very deep residual channel attention networks.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Image super-resolution using very deep residual channel attention networks

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T10:20:16.710435Z

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Observation b8575674-e7e0-47d0-a873-19a19b4815a3 · outbound

This paper cites Residual dense network for image super-resolution.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Residual dense network for image super-resolution

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T10:20:16.715586Z

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Observation 107ae0a5-98d5-4900-8028-5c6e44be82d4 · outbound

This paper cites Csrgan: medical image super-resolution using a generative adversarial network.

Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution Csrgan: medical image super-resolution using a generative adversarial network

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:20:16.938427Z

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