{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/image-super-resolution/papers/5","list_of":"/task/image-super-resolution","task":"Image Super-Resolution","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":16,"rows_per_page":100,"rows":[401,500],"of":1589,"counts":{"archive_papers_tagged":1589,"with_a_code_link":783,"where_syntology_ran_a_sample":188,"not_listed_spam_title":0,"listed":1589,"listed_where_code_ran":188,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":171,"every_run_a_failure_of_syntologys_instrument":17,"listed_with_a_run_with_no_instrument_failure":171,"listed_every_run_a_failure_of_syntologys_instrument":17,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/image-super-resolution","prev":"/task/image-super-resolution/papers/4","next":"/task/image-super-resolution/papers/6","papers":[{"url":"/paper/reconstructed-convolution-module-based-look","slug":"reconstructed-convolution-module-based-look","title":"Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-Resolution","date":"2023-07-17","arxiv_id":"2307.08544","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/reconstructed-convolution-module-based-look#ran","syntology_url":"https://syntology.ai/paper/2307.08544","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08544"}},"official":{"repos":["liuguandu/rc-lut"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dwa-differential-wavelet-amplifier-for-image-1","slug":"dwa-differential-wavelet-amplifier-for-image-1","title":"DWA: Differential Wavelet Amplifier for Image Super-Resolution","date":"2023-07-10","arxiv_id":"2307.04593","repositories_listed":1,"syntology":null},{"url":"/paper/desra-detect-and-delete-the-artifacts-of-gan","slug":"desra-detect-and-delete-the-artifacts-of-gan","title":"DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models","date":"2023-07-05","arxiv_id":"2307.02457","repositories_listed":1,"syntology":null},{"url":"/paper/spatio-temporal-perception-distortion-trade","slug":"spatio-temporal-perception-distortion-trade","title":"Spatio-Temporal Perception-Distortion Trade-off in Learned Video SR","date":"2023-07-04","arxiv_id":"2307.01556","repositories_listed":1,"syntology":null},{"url":"/paper/wavemixsr-a-resource-efficient-neural-network","slug":"wavemixsr-a-resource-efficient-neural-network","title":"WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution","date":"2023-07-01","arxiv_id":"2307.00430","repositories_listed":1,"syntology":null},{"url":"/paper/novel-hybrid-learning-algorithms-for-improved","slug":"novel-hybrid-learning-algorithms-for-improved","title":"Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems","date":"2023-06-27","arxiv_id":"2306.15341","repositories_listed":1,"syntology":null},{"url":"/paper/transmrsr-transformer-based-self-distilled","slug":"transmrsr-transformer-based-self-distilled","title":"TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution","date":"2023-06-11","arxiv_id":"2306.06669","repositories_listed":1,"syntology":null},{"url":"/paper/scale-guided-hypernetwork-for-blind-super","slug":"scale-guided-hypernetwork-for-blind-super","title":"Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment","date":"2023-06-04","arxiv_id":"2306.02398","repositories_listed":1,"syntology":null},{"url":"/paper/a-feature-reuse-framework-with-texture","slug":"a-feature-reuse-framework-with-texture","title":"A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-Resolution","date":"2023-06-02","arxiv_id":"2306.01500","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-deep-models-for-real-time-4k-image","slug":"efficient-deep-models-for-real-time-4k-image","title":"Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report","date":"2023-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/physics-informed-ensemble-representation-for","slug":"physics-informed-ensemble-representation-for","title":"Physics-Informed Ensemble Representation for Light-Field Image Super-Resolution","date":"2023-05-31","arxiv_id":"2305.20006","repositories_listed":1,"syntology":null},{"url":"/paper/bicubic-slim-slimmer-slimmest-designing-an","slug":"bicubic-slim-slimmer-slimmest-designing-an","title":"Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution Network","date":"2023-05-03","arxiv_id":"2305.02126","repositories_listed":1,"syntology":null},{"url":"/paper/ultra-sharp-single-image-super-resolution","slug":"ultra-sharp-single-image-super-resolution","title":"Ultra Sharp : Study of Single Image Super Resolution using Residual Dense Network","date":"2023-04-21","arxiv_id":"2304.10870","repositories_listed":1,"syntology":null},{"url":"/paper/ntire-2023-challenge-on-light-field-image","slug":"ntire-2023-challenge-on-light-field-image","title":"NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results","date":"2023-04-20","arxiv_id":"2304.10415","repositories_listed":1,"syntology":null},{"url":"/paper/omni-aggregation-networks-for-lightweight","slug":"omni-aggregation-networks-for-lightweight","title":"Omni Aggregation Networks for Lightweight Image Super-Resolution","date":"2023-04-20","arxiv_id":"2304.10244","repositories_listed":1,"syntology":null},{"url":"/paper/l1bsr-exploiting-detector-overlap-for-self","slug":"l1bsr-exploiting-detector-overlap-for-self","title":"L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery","date":"2023-04-14","arxiv_id":"2304.06871","repositories_listed":1,"syntology":null},{"url":"/paper/cabm-content-aware-bit-mapping-for-single","slug":"cabm-content-aware-bit-mapping-for-single","title":"CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input","date":"2023-04-13","arxiv_id":"2304.06454","repositories_listed":1,"syntology":null},{"url":"/paper/cross-view-hierarchy-network-for-stereo-image","slug":"cross-view-hierarchy-network-for-stereo-image","title":"Cross-View Hierarchy Network for Stereo Image Super-Resolution","date":"2023-04-13","arxiv_id":"2304.06236","repositories_listed":1,"syntology":null},{"url":"/paper/better-cmos-produces-clearer-images-learning","slug":"better-cmos-produces-clearer-images-learning","title":"Better \"CMOS\" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution","date":"2023-04-07","arxiv_id":"2304.03542","repositories_listed":1,"syntology":null},{"url":"/paper/dwa-differential-wavelet-amplifier-for-image","slug":"dwa-differential-wavelet-amplifier-for-image","title":"Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution","date":"2023-04-04","arxiv_id":"2304.01994","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":2,"n_no_contract":3,"n_pointer_only":5,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 2 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dwa-differential-wavelet-amplifier-for-image#ran","syntology_url":"https://syntology.ai/paper/2304.01994","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01994"}},"official":{"repos":["brian-moser/diwa"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hyperthumbnail-real-time-6k-image-rescaling","slug":"hyperthumbnail-real-time-6k-image-rescaling","title":"Real-time 6K Image Rescaling with Rate-distortion Optimization","date":"2023-04-03","arxiv_id":"2304.01064","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hyperthumbnail-real-time-6k-image-rescaling#ran","syntology_url":"https://syntology.ai/paper/2304.01064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01064"}},"official":{"repos":["abnervictor/hyperthumbnail"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sosr-source-free-image-super-resolution-with","slug":"sosr-source-free-image-super-resolution-with","title":"Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer","date":"2023-03-31","arxiv_id":"2303.17783","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sosr-source-free-image-super-resolution-with#ran","syntology_url":"https://syntology.ai/paper/2303.17783","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.17783"}},"official":{"repos":["shallowdream204/SODA-SR"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/implicit-diffusion-models-for-continuous","slug":"implicit-diffusion-models-for-continuous","title":"Implicit Diffusion Models for Continuous Super-Resolution","date":"2023-03-29","arxiv_id":"2303.16491","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":3,"n_ran_checked":3,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":10,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/implicit-diffusion-models-for-continuous#ran","syntology_url":"https://syntology.ai/paper/2303.16491","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16491"}},"official":{"repos":["ree1s/idm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/operational-neural-networks-for-efficient","slug":"operational-neural-networks-for-efficient","title":"Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-Resolution","date":"2023-03-29","arxiv_id":"2303.16636","repositories_listed":1,"syntology":null},{"url":"/paper/learning-generative-structure-prior-for-blind","slug":"learning-generative-structure-prior-for-blind","title":"Learning Generative Structure Prior for Blind Text Image Super-resolution","date":"2023-03-26","arxiv_id":"2303.14726","repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-transformer-designs-into","slug":"incorporating-transformer-designs-into","title":"Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution","date":"2023-03-25","arxiv_id":"2303.14324","repositories_listed":1,"syntology":null},{"url":"/paper/human-guided-ground-truth-generation-for","slug":"human-guided-ground-truth-generation-for","title":"Human Guided Ground-truth Generation for Realistic Image Super-resolution","date":"2023-03-23","arxiv_id":"2303.13069","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/human-guided-ground-truth-generation-for#ran","syntology_url":"https://syntology.ai/paper/2303.13069","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.13069"}},"official":{"repos":["chrisdud0257/hggt"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/srformer-permuted-self-attention-for-single","slug":"srformer-permuted-self-attention-for-single","title":"SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution","date":"2023-03-17","arxiv_id":"2303.09735","repositories_listed":1,"syntology":null},{"url":"/paper/resdiff-combining-cnn-and-diffusion-model-for","slug":"resdiff-combining-cnn-and-diffusion-model-for","title":"ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution","date":"2023-03-15","arxiv_id":"2303.08714","repositories_listed":1,"syntology":null},{"url":"/paper/recursive-generalization-transformer-for","slug":"recursive-generalization-transformer-for","title":"Recursive Generalization Transformer for Image Super-Resolution","date":"2023-03-11","arxiv_id":"2303.06373","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":4,"n_instrument":7,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 7 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/recursive-generalization-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2303.06373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.06373"}},"official":{"repos":["zhengchen1999/rgt"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/combination-of-single-and-multi-frame-image","slug":"combination-of-single-and-multi-frame-image","title":"Combination of Single and Multi-frame Image Super-resolution: An Analytical Perspective","date":"2023-03-06","arxiv_id":"2303.03212","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-explicit-modelling-of-image","slug":"efficient-and-explicit-modelling-of-image","title":"Efficient and Explicit Modelling of Image Hierarchies for Image Restoration","date":"2023-03-01","arxiv_id":"2303.00748","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":4,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/efficient-and-explicit-modelling-of-image#ran","syntology_url":"https://syntology.ai/paper/2303.00748","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.00748"}},"official":{"repos":["ofsoundof/grl-image-restoration"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/spatially-adaptive-feature-modulation-for","slug":"spatially-adaptive-feature-modulation-for","title":"Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution","date":"2023-02-27","arxiv_id":"2302.13800","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/spatially-adaptive-feature-modulation-for#ran","syntology_url":"https://syntology.ai/paper/2302.13800","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.13800"}},"official":{"repos":["sunny2109/safmn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-residual-dense-vision-transformer-for","slug":"a-residual-dense-vision-transformer-for","title":"A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning","date":"2023-02-22","arxiv_id":"2302.11184","repositories_listed":1,"syntology":null},{"url":"/paper/continuous-remote-sensing-image-super","slug":"continuous-remote-sensing-image-super","title":"Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Space","date":"2023-02-16","arxiv_id":"2302.08046","repositories_listed":1,"syntology":null},{"url":"/paper/learning-non-local-spatial-angular","slug":"learning-non-local-spatial-angular","title":"Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution","date":"2023-02-16","arxiv_id":"2302.08058","repositories_listed":1,"syntology":null},{"url":"/paper/hyperspectral-image-super-resolution-with-3","slug":"hyperspectral-image-super-resolution-with-3","title":"Hyperspectral Image Super Resolution with Real Unaligned RGB Guidance","date":"2023-02-13","arxiv_id":"2302.06298","repositories_listed":1,"syntology":null},{"url":"/paper/hypernetworks-build-implicit-neural","slug":"hypernetworks-build-implicit-neural","title":"Hypernetworks build Implicit Neural Representations of Sounds","date":"2023-02-09","arxiv_id":"2302.04959","repositories_listed":1,"syntology":null},{"url":"/paper/osrt-omnidirectional-image-super-resolution","slug":"osrt-omnidirectional-image-super-resolution","title":"OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer","date":"2023-02-07","arxiv_id":"2302.03453","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/osrt-omnidirectional-image-super-resolution#ran","syntology_url":"https://syntology.ai/paper/2302.03453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.03453"}},"official":{"repos":["fanghua-yu/osrt"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/image-restoration-with-mean-reverting","slug":"image-restoration-with-mean-reverting","title":"Image Restoration with Mean-Reverting Stochastic Differential Equations","date":"2023-01-27","arxiv_id":"2301.11699","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/image-restoration-with-mean-reverting#ran","syntology_url":"https://syntology.ai/paper/2301.11699","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.11699"}},"official":{"repos":["algolzw/image-restoration-sde"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/trainable-loss-weights-in-super-resolution","slug":"trainable-loss-weights-in-super-resolution","title":"Trainable Loss Weights in Super-Resolution","date":"2023-01-25","arxiv_id":"2301.10575","repositories_listed":1,"syntology":null},{"url":"/paper/image-super-resolution-using-efficient","slug":"image-super-resolution-using-efficient","title":"Image Super-Resolution using Efficient Striped Window Transformer","date":"2023-01-24","arxiv_id":"2301.09869","repositories_listed":1,"syntology":null},{"url":"/paper/is-autoencoder-truly-applicable-for-3d-ct","slug":"is-autoencoder-truly-applicable-for-3d-ct","title":"Is Autoencoder Truly Applicable for 3D CT Super-Resolution?","date":"2023-01-23","arxiv_id":"2302.10272","repositories_listed":1,"syntology":null},{"url":"/paper/fast-full-resolution-target-adaptive-cnn","slug":"fast-full-resolution-target-adaptive-cnn","title":"Fast Full-Resolution Target-Adaptive CNN-Based Pansharpening Framework","date":"2023-01-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/b-spline-texture-coefficients-estimator-for","slug":"b-spline-texture-coefficients-estimator-for","title":"B-Spline Texture Coefficients Estimator for Screen Content Image Super-Resolution","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/boosting-single-image-super-resolution-via","slug":"boosting-single-image-super-resolution-via","title":"Boosting Single Image Super-Resolution via Partial Channel Shifting","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cutmib-boosting-light-field-super-resolution","slug":"cutmib-boosting-light-field-super-resolution","title":"CutMIB: Boosting Light Field Super-Resolution via Multi-View Image Blending","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-arbitrary-scale-image-super-resolution","slug":"deep-arbitrary-scale-image-super-resolution","title":"Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance Pursuit","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-random-projector-accelerated-deep-image","slug":"deep-random-projector-accelerated-deep-image","title":"Deep Random Projector: Accelerated Deep Image Prior","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-correction-filter-via-degradation","slug":"learning-correction-filter-via-degradation","title":"Learning Correction Filter via Degradation-Adaptive Regression for Blind Single Image Super-Resolution","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lightweight-image-super-resolution-with-4","slug":"lightweight-image-super-resolution-with-4","title":"Lightweight Image Super-Resolution with Superpixel Token Interaction","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efficient-image-super-resolution-with-feature","slug":"efficient-image-super-resolution-with-feature","title":"Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network","date":"2022-12-29","arxiv_id":"2212.14181","repositories_listed":1,"syntology":null},{"url":"/paper/infrared-image-super-resolution-systematic","slug":"infrared-image-super-resolution-systematic","title":"Infrared Image Super-Resolution: Systematic Review, and Future Trends","date":"2022-12-22","arxiv_id":"2212.12322","repositories_listed":1,"syntology":null},{"url":"/paper/reference-based-image-and-video-super","slug":"reference-based-image-and-video-super","title":"Reference-based Image and Video Super-Resolution via C2-Matching","date":"2022-12-19","arxiv_id":"2212.09581","repositories_listed":1,"syntology":null},{"url":"/paper/meta-learned-kernel-for-blind-super","slug":"meta-learned-kernel-for-blind-super","title":"Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation","date":"2022-12-15","arxiv_id":"2212.07886","repositories_listed":1,"syntology":null},{"url":"/paper/source-aware-spatial-spectral-integrated","slug":"source-aware-spatial-spectral-integrated","title":"U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image Fusion","date":"2022-12-13","arxiv_id":"2212.06466","repositories_listed":1,"syntology":null},{"url":"/paper/ciaosr-continuous-implicit-attention-in","slug":"ciaosr-continuous-implicit-attention-in","title":"CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution","date":"2022-12-08","arxiv_id":"2212.04362","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-component-learning-with-degradation","slug":"bridging-component-learning-with-degradation","title":"Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution","date":"2022-12-03","arxiv_id":"2212.01628","repositories_listed":1,"syntology":null},{"url":"/paper/learning-detail-structure-alternative","slug":"learning-detail-structure-alternative","title":"Learning Detail-Structure Alternative Optimization for Blind Super-Resolution","date":"2022-12-03","arxiv_id":"2212.01624","repositories_listed":1,"syntology":null},{"url":"/paper/global-learnable-attention-for-single-image","slug":"global-learnable-attention-for-single-image","title":"Global Learnable Attention for Single Image Super-Resolution","date":"2022-12-02","arxiv_id":"2212.01057","repositories_listed":1,"syntology":null},{"url":"/paper/from-coarse-to-fine-hierarchical-pixel","slug":"from-coarse-to-fine-hierarchical-pixel","title":"From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-Resolution","date":"2022-11-30","arxiv_id":"2211.16776","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-distillation-based-degradation","slug":"knowledge-distillation-based-degradation","title":"Knowledge Distillation based Degradation Estimation for Blind Super-Resolution","date":"2022-11-30","arxiv_id":"2211.16928","repositories_listed":1,"syntology":null},{"url":"/paper/chimle-conditional-hierarchical-imle-for","slug":"chimle-conditional-hierarchical-imle-for","title":"CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis","date":"2022-11-25","arxiv_id":"2211.14286","repositories_listed":1,"syntology":null},{"url":"/paper/gan-prior-based-null-space-learning-for","slug":"gan-prior-based-null-space-learning-for","title":"GAN Prior based Null-Space Learning for Consistent Super-Resolution","date":"2022-11-24","arxiv_id":"2211.13524","repositories_listed":1,"syntology":null},{"url":"/paper/perception-oriented-single-image-super","slug":"perception-oriented-single-image-super","title":"Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation","date":"2022-11-24","arxiv_id":"2211.13676","repositories_listed":1,"syntology":null},{"url":"/paper/curvpnp-plug-and-play-blind-image-restoration","slug":"curvpnp-plug-and-play-blind-image-restoration","title":"CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature Denoiser","date":"2022-11-14","arxiv_id":"2211.07286","repositories_listed":1,"syntology":null},{"url":"/paper/the-best-of-both-worlds-a-framework-for","slug":"the-best-of-both-worlds-a-framework-for","title":"The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks","date":"2022-11-09","arxiv_id":"2211.05018","repositories_listed":1,"syntology":null},{"url":"/paper/single-image-super-resolution-via-a-dual","slug":"single-image-super-resolution-via-a-dual","title":"Single Image Super-Resolution via a Dual Interactive Implicit Neural Network","date":"2022-10-23","arxiv_id":"2210.12593","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/single-image-super-resolution-via-a-dual#ran","syntology_url":"https://syntology.ai/paper/2210.12593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12593"}},"official":{"repos":["robotic-vision-lab/dual-interactive-implicit-neural-network"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-image-super-resolution-using-vast","slug":"efficient-image-super-resolution-using-vast","title":"Efficient Image Super-Resolution using Vast-Receptive-Field Attention","date":"2022-10-12","arxiv_id":"2210.05960","repositories_listed":1,"syntology":null},{"url":"/paper/deep-fourier-up-sampling","slug":"deep-fourier-up-sampling","title":"Deep Fourier Up-Sampling","date":"2022-10-11","arxiv_id":"2210.05171","repositories_listed":1,"syntology":null},{"url":"/paper/mus2-a-benchmark-for-sentinel-2-multi-image","slug":"mus2-a-benchmark-for-sentinel-2-multi-image","title":"MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution","date":"2022-10-06","arxiv_id":"2210.02745","repositories_listed":1,"syntology":null},{"url":"/paper/single-image-super-resolution-based-on","slug":"single-image-super-resolution-based-on","title":"Single Image Super-Resolution Based on Capsule Neural Networks","date":"2022-10-06","arxiv_id":"2210.03743","repositories_listed":1,"syntology":null},{"url":"/paper/accurate-image-restoration-with-attention","slug":"accurate-image-restoration-with-attention","title":"Accurate Image Restoration with Attention Retractable Transformer","date":"2022-10-04","arxiv_id":"2210.01427","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/accurate-image-restoration-with-attention#ran","syntology_url":"https://syntology.ai/paper/2210.01427","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.01427"}},"official":{"repos":["gladzhang/art"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/from-face-to-natural-image-learning-real","slug":"from-face-to-natural-image-learning-real","title":"From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution","date":"2022-10-03","arxiv_id":"2210.00752","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/from-face-to-natural-image-learning-real#ran","syntology_url":"https://syntology.ai/paper/2210.00752","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.00752"}},"official":{"repos":["csxmli2016/redegnet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-scale-attention-network-for-image-super","slug":"multi-scale-attention-network-for-image-super","title":"Multi-scale Attention Network for Single Image Super-Resolution","date":"2022-09-28","arxiv_id":"2209.14145","repositories_listed":1,"syntology":null},{"url":"/paper/a-heterogeneous-group-cnn-for-image-super","slug":"a-heterogeneous-group-cnn-for-image-super","title":"A heterogeneous group CNN for image super-resolution","date":"2022-09-26","arxiv_id":"2209.12406","repositories_listed":1,"syntology":null},{"url":"/paper/real-rawvsr-real-world-raw-video-super","slug":"real-rawvsr-real-world-raw-video-super","title":"Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset","date":"2022-09-26","arxiv_id":"2209.12475","repositories_listed":1,"syntology":null},{"url":"/paper/face-super-resolution-using-stochastic","slug":"face-super-resolution-using-stochastic","title":"Face Super-Resolution Using Stochastic Differential Equations","date":"2022-09-24","arxiv_id":"2209.12064","repositories_listed":1,"syntology":null},{"url":"/paper/kxnet-a-model-driven-deep-neural-network-for","slug":"kxnet-a-model-driven-deep-neural-network-for","title":"KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution","date":"2022-09-21","arxiv_id":"2209.10305","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/kxnet-a-model-driven-deep-neural-network-for#ran","syntology_url":"https://syntology.ai/paper/2209.10305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.10305"}},"official":{"repos":["jiahong-fu/kxnet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-adversarial-super-resolution-at","slug":"generative-adversarial-super-resolution-at","title":"Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation","date":"2022-09-07","arxiv_id":"2209.03355","repositories_listed":1,"syntology":null},{"url":"/paper/joint-learning-content-and-degradation-aware","slug":"joint-learning-content-and-degradation-aware","title":"Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution","date":"2022-08-29","arxiv_id":"2208.13436","repositories_listed":1,"syntology":null},{"url":"/paper/dsr-towards-drone-image-super-resolution","slug":"dsr-towards-drone-image-super-resolution","title":"DSR: Towards Drone Image Super-Resolution","date":"2022-08-25","arxiv_id":"2208.12327","repositories_listed":1,"syntology":null},{"url":"/paper/at-ddpm-restoring-faces-degraded-by","slug":"at-ddpm-restoring-faces-degraded-by","title":"AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models","date":"2022-08-24","arxiv_id":"2208.11284","repositories_listed":1,"syntology":{"n":21,"n_ran":17,"n_constructed":0,"n_ran_checked":11,"n_instrument":6,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":9,"n_pointer_only":13,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 0 violated, 9 with no contract checked; 6 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/at-ddpm-restoring-faces-degraded-by#ran","syntology_url":"https://syntology.ai/paper/2208.11284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.11284"}},"official":{"repos":["nithin-gk/at-ddpm"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/rzsr-reference-based-zero-shot-super","slug":"rzsr-reference-based-zero-shot-super","title":"RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-Exemplars","date":"2022-08-24","arxiv_id":"2208.11313","repositories_listed":1,"syntology":null},{"url":"/paper/sliding-window-recurrent-network-for","slug":"sliding-window-recurrent-network-for","title":"Sliding Window Recurrent Network for Efficient Video Super-Resolution","date":"2022-08-24","arxiv_id":"2208.11608","repositories_listed":1,"syntology":null},{"url":"/paper/learning-degradation-representations-for","slug":"learning-degradation-representations-for","title":"Learning Degradation Representations for Image Deblurring","date":"2022-08-10","arxiv_id":"2208.05244","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":11,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/learning-degradation-representations-for#ran","syntology_url":"https://syntology.ai/paper/2208.05244","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.05244"}},"official":{"repos":["dasongli1/learning_degradation"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-local-implicit-image-function-for","slug":"adaptive-local-implicit-image-function-for","title":"Adaptive Local Implicit Image Function for Arbitrary-scale Super-resolution","date":"2022-08-07","arxiv_id":"2208.04318","repositories_listed":1,"syntology":null},{"url":"/paper/perception-distortion-balanced-admm","slug":"perception-distortion-balanced-admm","title":"Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution","date":"2022-08-05","arxiv_id":"2208.03324","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-degradation-radiograph-super","slug":"rethinking-degradation-radiograph-super","title":"Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN","date":"2022-08-05","arxiv_id":"2208.03008","repositories_listed":1,"syntology":null},{"url":"/paper/robust-real-world-image-super-resolution","slug":"robust-real-world-image-super-resolution","title":"Robust Real-World Image Super-Resolution against Adversarial Attacks","date":"2022-07-31","arxiv_id":"2208.00428","repositories_listed":1,"syntology":null},{"url":"/paper/glean-generative-latent-bank-for-image-super","slug":"glean-generative-latent-bank-for-image-super","title":"GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond","date":"2022-07-29","arxiv_id":"2207.14812","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/glean-generative-latent-bank-for-image-super#ran","syntology_url":"https://syntology.ai/paper/2207.14812","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.14812"}},"official":{"repos":["open-mmlab/mmediting"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-series-parallel-lookup-tables-for","slug":"learning-series-parallel-lookup-tables-for","title":"Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution","date":"2022-07-26","arxiv_id":"2207.12987","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/learning-series-parallel-lookup-tables-for#ran","syntology_url":"https://syntology.ai/paper/2207.12987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.12987"}},"official":{"repos":["zhjy2016/splut"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/reference-based-image-super-resolution-with","slug":"reference-based-image-super-resolution-with","title":"Reference-based Image Super-Resolution with Deformable Attention Transformer","date":"2022-07-25","arxiv_id":"2207.11938","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-image-rescaling-using-dual-latent","slug":"enhancing-image-rescaling-using-dual-latent","title":"Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network","date":"2022-07-24","arxiv_id":"2207.11844","repositories_listed":1,"syntology":null},{"url":"/paper/cadyq-content-aware-dynamic-quantization-for","slug":"cadyq-content-aware-dynamic-quantization-for","title":"CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution","date":"2022-07-21","arxiv_id":"2207.10345","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cadyq-content-aware-dynamic-quantization-for#ran","syntology_url":"https://syntology.ai/paper/2207.10345","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10345"}},"official":{"repos":["cheeun/cadyq"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-uncertainty-intervals-for","slug":"semantic-uncertainty-intervals-for","title":"Semantic uncertainty intervals for disentangled latent spaces","date":"2022-07-20","arxiv_id":"2207.10074","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semantic-uncertainty-intervals-for#ran","syntology_url":"https://syntology.ai/paper/2207.10074","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10074"}},"official":{"repos":["swamiviv/CLASP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/image-super-resolution-with-deep-dictionary","slug":"image-super-resolution-with-deep-dictionary","title":"Image Super-Resolution with Deep Dictionary","date":"2022-07-19","arxiv_id":"2207.09228","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":7,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 7 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 7 samples that ran constructed an object rather than computing a result","sample_list":"/paper/image-super-resolution-with-deep-dictionary#ran","syntology_url":"https://syntology.ai/paper/2207.09228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.09228"}},"official":{"repos":["shuntama/srdd"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/single-mr-image-super-resolution-using","slug":"single-mr-image-super-resolution-using","title":"Single MR Image Super-Resolution using Generative Adversarial Network","date":"2022-07-16","arxiv_id":"2207.08036","repositories_listed":1,"syntology":null},{"url":"/paper/quality-assessment-of-image-super-resolution","slug":"quality-assessment-of-image-super-resolution","title":"Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity","date":"2022-07-15","arxiv_id":"2207.08689","repositories_listed":1,"syntology":null},{"url":"/paper/bayescap-bayesian-identity-cap-for-calibrated","slug":"bayescap-bayesian-identity-cap-for-calibrated","title":"BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks","date":"2022-07-14","arxiv_id":"2207.06873","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/bayescap-bayesian-identity-cap-for-calibrated#ran","syntology_url":"https://syntology.ai/paper/2207.06873","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.06873"}},"official":{"repos":["explainableml/bayescap"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"5419821b5879959efca247c155117e8b26d741270fe4814daaf91a21ec4d4d32","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}