Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T00:49:39.681102Z
Paper Citation Record · LEDGER
Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution
As of 21 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 0 inbound Pith citation observations for arXiv:2506.12738.
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 2026-08-21T06:32:19.484+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
70 of 70 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
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
- Resolution
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- raw_fallback, observed 2026-08-07T00:49:51.477289Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
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- raw_fallback, observed 2026-08-07T00:49:51.162130Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 731aefcc-9fa2-4e0f-9146-777eb9fc28c3 · outbound
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
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:50.229943Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:49.942528Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:49.613446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:49.310945Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 74af84c1-5cc0-496d-835b-4a3eea0a1978 · outbound
This paper cites an unresolved cited work.
Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution Unresolved cited work
Reference 11
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:48.559617Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
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Source-reported events for the cited work
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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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 3b6aa6a9-2314-41cb-b6b6-9c5b66509689 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 59a1647c-33ae-4590-99fa-11070dd7f49b · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c69ea65e-f928-48aa-b6dc-9af74ea19b35 · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:46.967762Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:46.500108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:46.247760Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1f3a0eff-7157-405d-893e-b63c0aeee653 · outbound
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
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:45.996041Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:45.792560Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:45.524099Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:45.341054Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:45.121411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:44.938178Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:44.750310Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:44.596626Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:44.438518Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:44.291954Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:44.175507Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:44.026183Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d5a9c120-fb3e-478a-a7ba-6082b990c919 · outbound
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,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:43.880945Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:43.716115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:43.573541Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:43.437694Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:49:37.759437Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:49:37.850266Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:49:37.916198Z
Source-reported events for the cited work
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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
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:49:43.293985Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+00:00.
Pith citing papers
No inbound Pith citation observations are available.