Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution | Paper Citation Record

Observation fdcad4ed-6de3-44b0-8820-3cf2a1f0fa3a · outbound

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

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Ntire 2017 challenge on single image super-resolution: Dataset and study

Reference 1

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Observation 8fb82727-01eb-48ea-9973-fa4950a29a27 · outbound

This paper cites Blind super-resolution kernel estimation using an internal-gan.Ad- vances in Neural Information Processing Systems, 32, 2019.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Blind super-resolution kernel estimation using an internal-gan.Ad- vances in Neural Information Processing Systems, 32, 2019

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:51.162130Z

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This paper cites Revisiting resnets: Improved training and scaling strategies.Advances in Neural Information Process- ing Systems, 34:22614–22627, 2021.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Revisiting resnets: Improved training and scaling strategies.Advances in Neural Information Process- ing Systems, 34:22614–22627, 2021

Reference 3

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Observation 948939d8-0f2a-4d32-a806-24dd57687705 · outbound

This paper cites Low-complexity single-image super-resolution based on nonnegative neighbor embedding.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Low-complexity single-image super-resolution based on nonnegative neighbor embedding

Reference 4

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Observation 4840715c-91bc-4fe9-bc4d-c5a09a495444 · outbound

This paper cites Understanding batch normalization.Advances in Neural Information Processing Systems, 31, 2018.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Understanding batch normalization.Advances in Neural Information Processing Systems, 31, 2018

Reference 5

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Observation f6d346f6-ab67-4aed-a40e-5a548db50e14 · outbound

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

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Toward real-world single image super-resolution: A new benchmark and a new model

Reference 6

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Observation 5f41c811-6009-4007-97dc-bd8fede047dc · outbound

This paper cites Real-world blind super-resolution via feature matching with implicit high- resolution priors.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Real-world blind super-resolution via feature matching with implicit high- resolution priors

Reference 7

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Observation a692631f-554f-4860-813d-4ffe5a5b1e35 · outbound

This paper cites Masked image training for generalizable deep image denois- ing.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Masked image training for generalizable deep image denois- ing

Reference 8

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Observation 55a6013b-d990-476c-83df-ceedf1ff5ddb · outbound

This paper cites Activating more pixels in image super- resolution transformer.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Activating more pixels in image super- resolution transformer

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:49.174509Z

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Observation ad9e6eb7-ae07-4a61-acc0-3d62dd86d7b4 · outbound

This paper cites Adam: A method for stochastic opti- mization.(No Title), 2014.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Adam: A method for stochastic opti- mization.(No Title), 2014

Reference 10

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Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Unresolved cited work

Reference 11

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Observation 3d4f275e-7520-4730-8c0a-780bbd870a8f · outbound

This paper cites Dropblock: A regularization method for convolutional networks.Advances in Neural Information Processing Systems, 31, 2018.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Dropblock: A regularization method for convolutional networks.Advances in Neural Information Processing Systems, 31, 2018

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:48.680397Z

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Observation 535dff1d-3be4-4973-ada7-e1f655fe507a · outbound

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

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Blind super-resolution with iterative kernel correction

Reference 13

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Observation ae1ad645-1696-4683-99b0-bdda326dc080 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Masked autoencoders are scalable vision learners

Reference 14

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Observation 4d17f383-3fdf-424a-81cb-d0bfa8a3a28f · outbound

This paper cites DRCT: Saving Image Super-resolution away from Information Bottleneck.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution DRCT: Saving Image Super-resolution away from Information Bottleneck

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:35.596299Z

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Observation f7328497-d06a-41e0-9e63-dd31ad9eb2b8 · outbound

This paper cites Squeeze-and-excitation networks.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Squeeze-and-excitation networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:48.381364Z

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This paper cites Single image super-resolution from transformed self-exemplars.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Single image super-resolution from transformed self-exemplars

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:48.195380Z

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Observation 3810534c-b0c6-4b32-82e5-6b2e25fdb531 · outbound

This paper cites Un- folding the alternating optimization for blind super resolu- tion.Advances in Neural Information Processing Systems, 33:5632–5643, 2020.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Un- folding the alternating optimization for blind super resolu- tion.Advances in Neural Information Processing Systems, 33:5632–5643, 2020

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:47.983643Z

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This paper cites Learning degradation-invariant representation for ro- bust real-world person re-identification.International Jour- nal of Computer Vision, 130(11):2770–2796, 2022.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Learning degradation-invariant representation for ro- bust real-world person re-identification.International Jour- nal of Computer Vision, 130(11):2770–2796, 2022

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:47.832171Z

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Observation ce46def0-e1bd-453c-b714-52ef29bd0dc3 · outbound

This paper cites Structural and statistical texture knowledge distillation for semantic segmentation.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Structural and statistical texture knowledge distillation for semantic segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:47.697778Z

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This paper cites an unresolved cited work.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:49:47.553666Z

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Observation 7400a914-832b-49f0-838d-bb45d612654a · outbound

This paper cites Ultra-high resolution segmentation with ultra-rich con- text: A novel benchmark.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Ultra-high resolution segmentation with ultra-rich con- text: A novel benchmark

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:47.415647Z

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Observation cf800f84-331e-4da2-b63c-ba15ebde0ff4 · outbound

This paper cites Ppt- former: Pseudo multi-perspective transformer for uav seg- mentation.International Joint Conference on Artificial In- telligence, pages 893–901, 2024.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Ppt- former: Pseudo multi-perspective transformer for uav seg- mentation.International Joint Conference on Artificial In- telligence, pages 893–901, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:47.185817Z

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Observation 0c4256a2-fe2f-4ee5-b8b3-6d2424604ccf · outbound

This paper cites Discrete latent perspective learning for seg- mentation and detection.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Discrete latent perspective learning for seg- mentation and detection

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:46.967762Z

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Observation e85521c4-1615-4cd6-9b80-7fb86a60c52c · outbound

This paper cites Structural and statistical texture knowledge distillation and learning for segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–18, 2025.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Structural and statistical texture knowledge distillation and learning for segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–18, 2025

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:46.732431Z

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Observation 897f6761-7088-464f-9ce0-2ef997da5a81 · outbound

This paper cites Multi-scale progressive fusion network for single image deraining.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Multi-scale progressive fusion network for single image deraining

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:46.500108Z

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Observation 48c52dd6-1b85-4280-bc12-3f4310a1565f · outbound

This paper cites Inconsistency, instability, and generalization gap of deep neural network training.Ad- vances in Neural Information Processing Systems, 36, 2024.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Inconsistency, instability, and generalization gap of deep neural network training.Ad- vances in Neural Information Processing Systems, 36, 2024

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This paper cites Lightweight prompt learning implicit degradation estimation network for blind super resolution.IEEE Transactions on Image Processing, 2024.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Lightweight prompt learning implicit degradation estimation network for blind super resolution.IEEE Transactions on Image Processing, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:45.996041Z

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Observation c900f8c6-7609-4b6b-b868-044cb441b2c2 · outbound

This paper cites Reflash dropout in image super-resolution.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Reflash dropout in image super-resolution

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:45.792560Z

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Observation 9485469b-46cd-42b9-915a-4918ed303b7a · outbound

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

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Photo- realistic single image super-resolution using a generative ad- versarial network

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:45.524099Z

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Observation abc416a6-fe25-44c2-af4b-7365dba0eada · outbound

This paper cites Benchmarking single- image dehazing and beyond.IEEE Transactions on Image Processing, 28(1):492–505, 2019.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Benchmarking single- image dehazing and beyond.IEEE Transactions on Image Processing, 28(1):492–505, 2019

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Observation 566969b0-b6fa-4452-8798-c3f709c1d002 · outbound

This paper cites Learning detail-structure alternative opti- mization for blind super-resolution.IEEE Transactions on Multimedia, 25:2825–2838, 2022.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Learning detail-structure alternative opti- mization for blind super-resolution.IEEE Transactions on Multimedia, 25:2825–2838, 2022

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Observation 8854c1c4-2b9e-4ffd-9b75-5cce6d63dd76 · outbound

This paper cites Under- standing the disharmony between dropout and batch normal- ization by variance shift.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Under- standing the disharmony between dropout and batch normal- ization by variance shift

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Observation 3703a8bb-377b-4e8a-8e14-31f4fa1ffeb0 · outbound

This paper cites Swinir: Image restoration us- ing swin transformer.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Swinir: Image restoration us- ing swin transformer

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:44.750310Z

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Observation dde444fc-68fa-43d1-ae39-b18db5d14608 · outbound

This paper cites Flow-based kernel prior with application to blind super-resolution.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Flow-based kernel prior with application to blind super-resolution

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:44.596626Z

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Observation dee2752b-b52f-4543-83e6-01342e8dd445 · outbound

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

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Efficient and degradation-adaptive network for real-world image super- resolution

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:44.438518Z

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Observation 7f024fd0-e232-4ca1-91ea-3cd81375423f · outbound

This paper cites Blind image super-resolution: A survey and beyond.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):5461–5480, 2022.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Blind image super-resolution: A survey and beyond.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):5461–5480, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:44.291954Z

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Observation feeaf802-419c-4f56-bf2c-6d56caebb58e · outbound

This paper cites Degradation-invariant enhance- ment of fundus images via pyramid constraint network.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Degradation-invariant enhance- ment of fundus images via pyramid constraint network

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:44.175507Z

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Observation d7b5f792-714e-43ac-8b32-7548909f99de · outbound

This paper cites Evaluating the generalization ability of super- resolution networks.IEEE Transactions on pattern analysis and machine intelligence, 2023.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Evaluating the generalization ability of super- resolution networks.IEEE Transactions on pattern analysis and machine intelligence, 2023

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This paper cites Transferable representation learning with deep adaptation networks.IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 41(12):3071–3085,.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Transferable representation learning with deep adaptation networks.IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 41(12):3071–3085,

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Observation 74d6bb23-d869-4458-877d-344f2ef71513 · outbound

This paper cites A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:43.716115Z

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Observation a9bddb93-dc29-46bc-8530-3681dfcc6af5 · outbound

This paper cites Sketch-based manga retrieval using manga109 dataset.Mul- timedia tools and applications, 76:21811–21838, 2017.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Sketch-based manga retrieval using manga109 dataset.Mul- timedia tools and applications, 76:21811–21838, 2017

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:43.573541Z

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Observation d4f65be3-f462-4ab6-a6ed-44ffdb911ea7 · outbound

This paper cites Nonparametric blind super-resolution.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Nonparametric blind super-resolution

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:43.437694Z

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Observation 010c2bb7-eeda-4b95-a4a7-254f31cd3dea · outbound

This paper cites On the importance of single directions for generalization.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution On the importance of single directions for generalization

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Observation 4a5c3d40-e4e9-498a-b8d2-13c09b901be3 · outbound

This paper cites Implicit Regularization in Deep Learning.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Implicit Regularization in Deep Learning

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Observation 8d184fc1-45fd-4671-b838-b26af90a9fa3 · outbound

This paper cites In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

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Observation 5ccf67f7-a806-4cc3-8f57-61268cd91e5d · outbound

This paper cites Super-resolution of remote sensing imagery using implicit degradation modeling.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Super-resolution of remote sensing imagery using implicit degradation modeling

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:43.293985Z

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Observation 60943f8b-e407-4477-8a99-d7a8faaba7b5 · outbound

This paper cites Effect of dropout layer on clas- sical regression problems.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Effect of dropout layer on clas- sical regression problems

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:43.156543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9d3401be-3bd5-425c-8ed3-d3c5ed8419ea · outbound

This paper cites Pointnext: Revisiting pointnet++ with improved training and scaling strategies.Advances in Neural Informa- tion Processing Systems, 35:23192–23204, 2022.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Pointnext: Revisiting pointnet++ with improved training and scaling strategies.Advances in Neural Informa- tion Processing Systems, 35:23192–23204, 2022

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:42.977418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 35968880-c500-498e-a316-7cd9c27e433e · outbound

This paper cites Ffa-net: Feature fusion attention network for single image dehazing.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Ffa-net: Feature fusion attention network for single image dehazing

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:42.827388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6458f75f-e839-400b-a2eb-f76c03e2bb27 · outbound

This paper cites Multi-degradation super- 10 resolution reconstruction for remote sensing images with re- construction features-guided kernel correction.Remote Sens- ing, 16(16):2915, 2024.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Multi-degradation super- 10 resolution reconstruction for remote sensing images with re- construction features-guided kernel correction.Remote Sens- ing, 16(16):2915, 2024

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:42.675209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e660317d-907f-44ce-8a3b-6a49fce81b3a · outbound

This paper cites Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:49:39.879224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 75cf674a-c12d-477b-8970-64f15cfe3f09 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958, 2014.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958, 2014

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:42.511443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5d8bfbf2-762d-4aa9-9038-10e898cd02cf · outbound

This paper cites Ntire 2017 challenge on single image super-resolution: Methods and results.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Ntire 2017 challenge on single image super-resolution: Methods and results

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:42.366298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d4f1a260-a969-47d3-a400-9ef017f21bed · outbound

This paper cites Rcdnet: An interpretable rain convolutional dictionary network for single image de- raining.IEEE Transactions on Neural Networks and Learn- ing Systems, 2023.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Rcdnet: An interpretable rain convolutional dictionary network for single image de- raining.IEEE Transactions on Neural Networks and Learn- ing Systems, 2023

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:42.197281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9eccde62-0b0c-4c2b-99be-99ab5abda873 · outbound

This paper cites Navigating beyond dropout: An intriguing solution towards generalizable image super resolution.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Navigating beyond dropout: An intriguing solution towards generalizable image super resolution

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:42.035580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bb6fcd2e-f3e8-4b06-9815-bda9313c4428 · outbound

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

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Real-esrgan: Training real-world blind super-resolution with pure synthetic data

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:38.767764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38e8a9aa-74e7-4aed-a609-24a596662105 · outbound

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

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Component divide- and-conquer for real-world image super-resolution

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:41.884722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e1287f0b-9a53-4d48-8ca5-29a53cc0e379 · outbound

This paper cites R-drop: Regularized dropout for neural networks.Advances in Neural Informa- tion Processing Systems, 34:10890–10905, 2021.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution R-drop: Regularized dropout for neural networks.Advances in Neural Informa- tion Processing Systems, 34:10890–10905, 2021

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:41.739926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 633789f8-778f-42fe-8705-fab5407220d0 · outbound

This paper cites Group normalization.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Group normalization

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:41.583661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7381c6aa-a5dd-493c-bf50-22ae2f87a2a1 · outbound

This paper cites Understanding and improving layer normaliza- tion.Advances in Neural Information Processing Systems, 32, 2019.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Understanding and improving layer normaliza- tion.Advances in Neural Information Processing Systems, 32, 2019

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:41.417399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d1c16b62-0221-4990-ac2b-821289b4940a · outbound

This paper cites Kgsr: A kernel guided net- work for real-world blind super-resolution.Pattern Recogni- tion, 147:110095, 2024.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Kgsr: A kernel guided net- work for real-world blind super-resolution.Pattern Recogni- tion, 147:110095, 2024

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:41.272153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2eeb52db-87d4-4682-8b07-b3b442f8ee73 · outbound

This paper cites Image super-resolution via sparse representation.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Image super-resolution via sparse representation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:41.120650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aaad7679-2d33-4bd9-a093-1781e177e1ed · outbound

This paper cites How transferable are features in deep neural networks?Ad- vances in Neural Information Processing Systems, 27, 2014.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution How transferable are features in deep neural networks?Ad- vances in Neural Information Processing Systems, 27, 2014

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:40.949851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 93ac655b-7480-45eb-b71a-10ca8469a019 · outbound

This paper cites Blind image super-resolution with elaborate degradation modeling on noise and kernel.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Blind image super-resolution with elaborate degradation modeling on noise and kernel

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:40.767191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2b48202c-0805-4d70-8dde-87bb1571eb72 · outbound

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

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Resshift: Efficient diffusion model for image super- resolution by residual shifting.Advances in Neural Infor- mation Processing Systems, 36:13294–13307, 2023

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:40.635809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 44235187-587b-4c72-9871-75216e753a86 · outbound

This paper cites Restormer: Efficient transformer for high-resolution image restoration.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Restormer: Efficient transformer for high-resolution image restoration

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:40.517098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a7a07dae-1c92-464d-b002-b376fce12363 · outbound

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

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Designing a practical degradation model for deep blind image super-resolution

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:40.359887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 766c7a0a-869e-4dab-abf2-ad8b1c7871c8 · outbound

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

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Image super-resolution using very deep residual channel attention networks

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:40.242357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2922517a-fbdc-40c3-bf6e-ae7ba49a845c · outbound

This paper cites Weakly-supervised con- trastive learning-based implicit degradation modeling for blind image super-resolution.Knowledge-Based Systems, 249:108984, 2022.

Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Weakly-supervised con- trastive learning-based implicit degradation modeling for blind image super-resolution.Knowledge-Based Systems, 249:108984, 2022

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:49:40.098962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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