{"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/super-resolution/papers/12","list_of":"/task/super-resolution","task":"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":12,"pages_in_order":39,"rows_per_page":100,"rows":[1101,1200],"of":3874,"counts":{"archive_papers_tagged":3874,"with_a_code_link":1627,"where_syntology_ran_a_sample":382,"not_listed_spam_title":0,"listed":3874,"listed_where_code_ran":382,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":343,"every_run_a_failure_of_syntologys_instrument":39,"listed_with_a_run_with_no_instrument_failure":343,"listed_every_run_a_failure_of_syntologys_instrument":39,"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/super-resolution","prev":"/task/super-resolution/papers/11","next":"/task/super-resolution/papers/13","papers":[{"url":"/paper/hybrid-pixel-unshuffled-network-for","slug":"hybrid-pixel-unshuffled-network-for","title":"Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution","date":"2022-03-16","arxiv_id":"2203.08921","repositories_listed":1,"syntology":null},{"url":"/paper/learning-the-dynamics-of-physical-systems-1","slug":"learning-the-dynamics-of-physical-systems-1","title":"Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks","date":"2022-03-16","arxiv_id":"2203.08852","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":0,"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-the-dynamics-of-physical-systems-1#ran","syntology_url":"https://syntology.ai/paper/2203.08852","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08852"}},"official":{"repos":["martenlienen/finite-element-networks"],"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/panini-net-gan-prior-based-degradation-aware","slug":"panini-net-gan-prior-based-degradation-aware","title":"Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face Restoration","date":"2022-03-16","arxiv_id":"2203.08444","repositories_listed":1,"syntology":null},{"url":"/paper/rich-cnn-transformer-feature-aggregation","slug":"rich-cnn-transformer-feature-aggregation","title":"Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution","date":"2022-03-15","arxiv_id":"2203.07682","repositories_listed":1,"syntology":null},{"url":"/paper/stdan-deformable-attention-network-for-space","slug":"stdan-deformable-attention-network-for-space","title":"STDAN: Deformable Attention Network for Space-Time Video Super-Resolution","date":"2022-03-14","arxiv_id":"2203.06841","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-long-range-attention-network-for","slug":"efficient-long-range-attention-network-for","title":"Efficient Long-Range Attention Network for Image Super-resolution","date":"2022-03-13","arxiv_id":"2203.06697","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":6,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 6 samples that ran constructed an object rather than computing a result","sample_list":"/paper/efficient-long-range-attention-network-for#ran","syntology_url":"https://syntology.ai/paper/2203.06697","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.06697"}},"official":{"repos":["xindongzhang/elan"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unfolded-deep-kernel-estimation-for-blind","slug":"unfolded-deep-kernel-estimation-for-blind","title":"Unfolded Deep Kernel Estimation for Blind Image Super-resolution","date":"2022-03-10","arxiv_id":"2203.05568","repositories_listed":1,"syntology":{"n":17,"n_ran":12,"n_constructed":9,"n_ran_checked":9,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":17,"phrase":"12 ran (of which 9 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/unfolded-deep-kernel-estimation-for-blind#ran","syntology_url":"https://syntology.ai/paper/2203.05568","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05568"}},"official":{"repos":["natezhenghy/udke"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":9,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-the-degradation-distribution-for","slug":"learning-the-degradation-distribution-for","title":"Learning the Degradation Distribution for Blind Image Super-Resolution","date":"2022-03-09","arxiv_id":"2203.04962","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-dual-trainable-bounds-for-ultra-low","slug":"dynamic-dual-trainable-bounds-for-ultra-low","title":"Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks","date":"2022-03-08","arxiv_id":"2203.03844","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":2,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"5 ran (of which 2 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) · 2 unverified","sample_list":"/paper/dynamic-dual-trainable-bounds-for-ultra-low#ran","syntology_url":"https://syntology.ai/paper/2203.03844","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.03844"}},"official":{"repos":["zysxmu/ddtb"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/regularized-training-of-intermediate-layers","slug":"regularized-training-of-intermediate-layers","title":"Regularized Training of Intermediate Layers for Generative Models for Inverse Problems","date":"2022-03-08","arxiv_id":"2203.04382","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-cross-layer-attention-for-image-1","slug":"adaptive-cross-layer-attention-for-image-1","title":"Adaptive Cross-Layer Attention for Image Restoration","date":"2022-03-04","arxiv_id":"2203.03619","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/adaptive-cross-layer-attention-for-image-1#ran","syntology_url":"https://syntology.ai/paper/2203.03619","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.03619"}},"official":{"repos":["sdl-asu/acla"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/hypertransformer-a-textural-and-spectral","slug":"hypertransformer-a-textural-and-spectral","title":"HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening","date":"2022-03-04","arxiv_id":"2203.02503","repositories_listed":1,"syntology":null},{"url":"/paper/ad2attack-adaptive-adversarial-attack-on-real","slug":"ad2attack-adaptive-adversarial-attack-on-real","title":"Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking","date":"2022-03-03","arxiv_id":"2203.01516","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-urban-flow-inference-with","slug":"fine-grained-urban-flow-inference-with","title":"Fine-grained Urban Flow Inference with Incomplete Data","date":"2022-03-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/spatio-temporal-vision-transformer-for-super","slug":"spatio-temporal-vision-transformer-for-super","title":"Spatio-temporal Vision Transformer for Super-resolution Microscopy","date":"2022-02-28","arxiv_id":"2203.00030","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-simulation-using-diffusion","slug":"conditional-simulation-using-diffusion","title":"Conditional Simulation Using Diffusion Schrödinger Bridges","date":"2022-02-27","arxiv_id":"2202.13460","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/conditional-simulation-using-diffusion#ran","syntology_url":"https://syntology.ai/paper/2202.13460","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.13460"}},"official":{"repos":["vdeborto/cdsb"],"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/super-resolution-gans-of-randomly-seeded","slug":"super-resolution-gans-of-randomly-seeded","title":"Super-resolution GANs of randomly-seeded fields","date":"2022-02-23","arxiv_id":"2202.11701","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-neural-network-modelling-for","slug":"convolutional-neural-network-modelling-for","title":"Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution","date":"2022-02-22","arxiv_id":"2202.10753","repositories_listed":1,"syntology":null},{"url":"/paper/ddos-unet-incorporating-temporal-information","slug":"ddos-unet-incorporating-temporal-information","title":"DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI","date":"2022-02-10","arxiv_id":"2202.05355","repositories_listed":1,"syntology":null},{"url":"/paper/mining-the-manifolds-of-deep-generative","slug":"mining-the-manifolds-of-deep-generative","title":"Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems","date":"2022-02-10","arxiv_id":"2202.05311","repositories_listed":1,"syntology":null},{"url":"/paper/trained-model-in-supervised-deep-learning-is","slug":"trained-model-in-supervised-deep-learning-is","title":"Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer","date":"2022-02-08","arxiv_id":"2202.03674","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-face-swap-method-for-image-and-video","slug":"a-new-face-swap-method-for-image-and-video","title":"A new face swap method for image and video domains: a technical report","date":"2022-02-07","arxiv_id":"2202.03046","repositories_listed":1,"syntology":null},{"url":"/paper/patch-based-stochastic-attention-for-image","slug":"patch-based-stochastic-attention-for-image","title":"Patch-Based Stochastic Attention for Image Editing","date":"2022-02-07","arxiv_id":"2202.03163","repositories_listed":1,"syntology":null},{"url":"/paper/tr-misr-multiimage-super-resolution-based-on","slug":"tr-misr-multiimage-super-resolution-based-on","title":"TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers","date":"2022-02-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/gradient-variance-loss-for-structure-enhanced","slug":"gradient-variance-loss-for-structure-enhanced","title":"Gradient Variance Loss for Structure-Enhanced Image Super-Resolution","date":"2022-02-02","arxiv_id":"2202.00997","repositories_listed":1,"syntology":null},{"url":"/paper/proximal-denoiser-for-convergent-plug-and","slug":"proximal-denoiser-for-convergent-plug-and","title":"Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization","date":"2022-01-31","arxiv_id":"2201.13256","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/proximal-denoiser-for-convergent-plug-and#ran","syntology_url":"https://syntology.ai/paper/2201.13256","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.13256"}},"official":{"repos":["samuro95/prox-pnp"],"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/end-to-end-optimization-of-metasurfaces-for","slug":"end-to-end-optimization-of-metasurfaces-for","title":"End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing","date":"2022-01-28","arxiv_id":"2201.12348","repositories_listed":1,"syntology":null},{"url":"/paper/vrt-a-video-restoration-transformer","slug":"vrt-a-video-restoration-transformer","title":"VRT: A Video Restoration Transformer","date":"2022-01-28","arxiv_id":"2201.12288","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/vrt-a-video-restoration-transformer#ran","syntology_url":"https://syntology.ai/paper/2201.12288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.12288"}},"official":{"repos":["jingyunliang/vrt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/denoising-diffusion-restoration-models","slug":"denoising-diffusion-restoration-models","title":"Denoising Diffusion Restoration Models","date":"2022-01-27","arxiv_id":"2201.11793","repositories_listed":1,"syntology":null},{"url":"/paper/learning-multiple-probabilistic-degradation","slug":"learning-multiple-probabilistic-degradation","title":"Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super Resolution","date":"2022-01-26","arxiv_id":"2201.10747","repositories_listed":1,"syntology":null},{"url":"/paper/hyperspectral-image-super-resolution-with-2","slug":"hyperspectral-image-super-resolution-with-2","title":"Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion","date":"2022-01-24","arxiv_id":"2201.09851","repositories_listed":1,"syntology":null},{"url":"/paper/perceptual-cgan-for-mri-super-resolution","slug":"perceptual-cgan-for-mri-super-resolution","title":"Perceptual cGAN for MRI Super-resolution","date":"2022-01-23","arxiv_id":"2201.09314","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-deep-blind-video-super","slug":"self-supervised-deep-blind-video-super","title":"Self-Supervised Deep Blind Video Super-Resolution","date":"2022-01-19","arxiv_id":"2201.07422","repositories_listed":1,"syntology":null},{"url":"/paper/cisrnet-compressed-image-super-resolution","slug":"cisrnet-compressed-image-super-resolution","title":"CISRNet: Compressed Image Super-Resolution Network","date":"2022-01-16","arxiv_id":"2201.06045","repositories_listed":1,"syntology":null},{"url":"/paper/sdt-dcscn-for-simultaneous-super-resolution","slug":"sdt-dcscn-for-simultaneous-super-resolution","title":"SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images","date":"2022-01-15","arxiv_id":"2201.05865","repositories_listed":1,"syntology":null},{"url":"/paper/flexible-style-image-super-resolution-using","slug":"flexible-style-image-super-resolution-using","title":"Flexible Style Image Super-Resolution using Conditional Objective","date":"2022-01-13","arxiv_id":"2201.04898","repositories_listed":1,"syntology":null},{"url":"/paper/coarse-to-fine-embedded-patchmatch-and-multi","slug":"coarse-to-fine-embedded-patchmatch-and-multi","title":"Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolution","date":"2022-01-12","arxiv_id":"2201.04358","repositories_listed":1,"syntology":null},{"url":"/paper/movidnn-a-mobile-platform-for-evaluating","slug":"movidnn-a-mobile-platform-for-evaluating","title":"MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural Networks","date":"2022-01-12","arxiv_id":"2201.04402","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-non-local-contrastive-attention-for","slug":"efficient-non-local-contrastive-attention-for","title":"Efficient Non-Local Contrastive Attention for Image Super-Resolution","date":"2022-01-11","arxiv_id":"2201.03794","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":1,"n_ran_checked":3,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-non-local-contrastive-attention-for#ran","syntology_url":"https://syntology.ai/paper/2201.03794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.03794"}},"official":{"repos":["zj-binxia/enlca"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/image-quality-measurements-and-denoising","slug":"image-quality-measurements-and-denoising","title":"Image quality measurements and denoising using Fourier Ring Correlations","date":"2022-01-11","arxiv_id":"2201.03992","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/image-quality-measurements-and-denoising#ran","syntology_url":"https://syntology.ai/paper/2201.03992","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.03992"}},"official":{"repos":["frccvpr/frc-loss"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mocopnet-exploring-local-motion-and-contrast","slug":"mocopnet-exploring-local-motion-and-contrast","title":"Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-Resolution","date":"2022-01-04","arxiv_id":"2201.01014","repositories_listed":1,"syntology":null},{"url":"/paper/detail-preserving-transformer-for-light-field","slug":"detail-preserving-transformer-for-light-field","title":"Detail-Preserving Transformer for Light Field Image Super-Resolution","date":"2022-01-02","arxiv_id":"2201.00346","repositories_listed":1,"syntology":{"n":21,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":21,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 4 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/detail-preserving-transformer-for-light-field#ran","syntology_url":"https://syntology.ai/paper/2201.00346","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.00346"}},"official":{"repos":["bitszwang/dpt"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/lar-sr-a-local-autoregressive-model-for-image","slug":"lar-sr-a-local-autoregressive-model-for-image","title":"LAR-SR: A Local Autoregressive Model for Image Super-Resolution","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learnable-lookup-table-for-neural-network","slug":"learnable-lookup-table-for-neural-network","title":"Learnable Lookup Table for Neural Network Quantization","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pix2nerf-unsupervised-conditional-p-gan-for-1","slug":"pix2nerf-unsupervised-conditional-p-gan-for-1","title":"Pix2NeRF: Unsupervised Conditional p-GAN for Single Image to Neural Radiance Fields Translation","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efficient-single-image-super-resolution-using-1","slug":"efficient-single-image-super-resolution-using-1","title":"Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning","date":"2021-12-31","arxiv_id":"2112.15386","repositories_listed":1,"syntology":null},{"url":"/paper/hprn-holistic-prior-embedded-relation-network","slug":"hprn-holistic-prior-embedded-relation-network","title":"HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution","date":"2021-12-29","arxiv_id":"2112.14608","repositories_listed":1,"syntology":null},{"url":"/paper/super-efficient-super-resolution-for-fast","slug":"super-efficient-super-resolution-for-fast","title":"Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge","date":"2021-12-29","arxiv_id":"2112.14340","repositories_listed":1,"syntology":null},{"url":"/paper/reflash-dropout-in-image-super-resolution","slug":"reflash-dropout-in-image-super-resolution","title":"Reflash Dropout in Image Super-Resolution","date":"2021-12-22","arxiv_id":"2112.12089","repositories_listed":1,"syntology":null},{"url":"/paper/a-esrgan-training-real-world-blind-super","slug":"a-esrgan-training-real-world-blind-super","title":"A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net Discriminators","date":"2021-12-19","arxiv_id":"2112.10046","repositories_listed":1,"syntology":null},{"url":"/paper/on-efficient-transformer-and-image-pre","slug":"on-efficient-transformer-and-image-pre","title":"On Efficient Transformer-Based Image Pre-training for Low-Level Vision","date":"2021-12-19","arxiv_id":"2112.10175","repositories_listed":1,"syntology":null},{"url":"/paper/pixel-distillation-a-new-knowledge","slug":"pixel-distillation-a-new-knowledge","title":"Pixel Distillation: A New Knowledge Distillation Scheme for Low-Resolution Image Recognition","date":"2021-12-17","arxiv_id":"2112.09532","repositories_listed":1,"syntology":null},{"url":"/paper/feature-distillation-interaction-weighting","slug":"feature-distillation-interaction-weighting","title":"Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution","date":"2021-12-16","arxiv_id":"2112.08655","repositories_listed":1,"syntology":null},{"url":"/paper/stable-long-term-recurrent-video-super","slug":"stable-long-term-recurrent-video-super","title":"Stable Long-Term Recurrent Video Super-Resolution","date":"2021-12-16","arxiv_id":"2112.08950","repositories_listed":1,"syntology":null},{"url":"/paper/kernel-aware-raw-burst-blind-super-resolution","slug":"kernel-aware-raw-burst-blind-super-resolution","title":"Kernel-aware Burst Blind Super-Resolution","date":"2021-12-14","arxiv_id":"2112.07315","repositories_listed":1,"syntology":null},{"url":"/paper/image-reconstruction-from-events-why-learn-it","slug":"image-reconstruction-from-events-why-learn-it","title":"Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow","date":"2021-12-12","arxiv_id":"2112.06242","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/image-reconstruction-from-events-why-learn-it#ran","syntology_url":"https://syntology.ai/paper/2112.06242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.06242"}},"official":{"repos":["tub-rip/event_based_image_rec_inverse_problem"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/implicit-transformer-network-for-screen-1","slug":"implicit-transformer-network-for-screen-1","title":"Implicit Transformer Network for Screen Content Image Continuous Super-Resolution","date":"2021-12-12","arxiv_id":"2112.06174","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/implicit-transformer-network-for-screen-1#ran","syntology_url":"https://syntology.ai/paper/2112.06174","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.06174"}},"official":{"repos":["codyshen0000/itsrn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-dynamic-residual-self-attention-network-for","slug":"a-dynamic-residual-self-attention-network-for","title":"A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution","date":"2021-12-08","arxiv_id":"2112.04488","repositories_listed":1,"syntology":null},{"url":"/paper/a-dataset-free-self-supervised-disentangled","slug":"a-dataset-free-self-supervised-disentangled","title":"Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image Fusion","date":"2021-12-06","arxiv_id":"2112.02869","repositories_listed":1,"syntology":null},{"url":"/paper/label-efficient-semantic-segmentation-with-1","slug":"label-efficient-semantic-segmentation-with-1","title":"Label-Efficient Semantic Segmentation with Diffusion Models","date":"2021-12-06","arxiv_id":"2112.03126","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":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) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/label-efficient-semantic-segmentation-with-1#ran","syntology_url":"https://syntology.ai/paper/2112.03126","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.03126"}},"official":{"repos":["yandex-research/ddpm-segmentation"],"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/pp-msvsr-multi-stage-video-super-resolution","slug":"pp-msvsr-multi-stage-video-super-resolution","title":"PP-MSVSR: Multi-Stage Video Super-Resolution","date":"2021-12-06","arxiv_id":"2112.02828","repositories_listed":1,"syntology":null},{"url":"/paper/towards-super-resolution-cest-mri-for","slug":"towards-super-resolution-cest-mri-for","title":"Towards Super-Resolution CEST MRI for Visualization of Small Structures","date":"2021-12-03","arxiv_id":"2112.01905","repositories_listed":1,"syntology":null},{"url":"/paper/fast-neural-representations-for-direct-volume","slug":"fast-neural-representations-for-direct-volume","title":"Fast Neural Representations for Direct Volume Rendering","date":"2021-12-02","arxiv_id":"2112.01579","repositories_listed":1,"syntology":null},{"url":"/paper/aligned-structured-sparsity-learning-for","slug":"aligned-structured-sparsity-learning-for","title":"Aligned Structured Sparsity Learning for Efficient Image Super-Resolution","date":"2021-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/conditional-generation-using-polynomial","slug":"conditional-generation-using-polynomial","title":"Conditional Generation Using Polynomial Expansions","date":"2021-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-temporal-alignment-for-video","slug":"revisiting-temporal-alignment-for-video","title":"Revisiting Temporal Alignment for Video Restoration","date":"2021-11-30","arxiv_id":"2111.15288","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/revisiting-temporal-alignment-for-video#ran","syntology_url":"https://syntology.ai/paper/2111.15288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.15288"}},"official":{"repos":["redrock303/revisiting-temporal-alignment-for-video-restoration"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/samplingaug-on-the-importance-of-patch","slug":"samplingaug-on-the-importance-of-patch","title":"SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution","date":"2021-11-30","arxiv_id":"2111.15185","repositories_listed":1,"syntology":null},{"url":"/paper/a-practical-contrastive-learning-framework","slug":"a-practical-contrastive-learning-framework","title":"A Practical Contrastive Learning Framework for Single-Image Super-Resolution","date":"2021-11-27","arxiv_id":"2111.13924","repositories_listed":1,"syntology":null},{"url":"/paper/adadm-enabling-normalization-for-image-super","slug":"adadm-enabling-normalization-for-image-super","title":"AdaDM: Enabling Normalization for Image Super-Resolution","date":"2021-11-27","arxiv_id":"2111.13905","repositories_listed":1,"syntology":null},{"url":"/paper/isnas-dip-image-specific-neural-architecture","slug":"isnas-dip-image-specific-neural-architecture","title":"ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior","date":"2021-11-27","arxiv_id":"2111.15362","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-tradeoffs-in-real-world-video","slug":"investigating-tradeoffs-in-real-world-video","title":"Investigating Tradeoffs in Real-World Video Super-Resolution","date":"2021-11-24","arxiv_id":"2111.12704","repositories_listed":1,"syntology":null},{"url":"/paper/advancing-high-resolution-video-language","slug":"advancing-high-resolution-video-language","title":"Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions","date":"2021-11-19","arxiv_id":"2111.10337","repositories_listed":1,"syntology":null},{"url":"/paper/local-texture-estimator-for-implicit","slug":"local-texture-estimator-for-implicit","title":"Local Texture Estimator for Implicit Representation Function","date":"2021-11-17","arxiv_id":"2111.08918","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/local-texture-estimator-for-implicit#ran","syntology_url":"https://syntology.ai/paper/2111.08918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.08918"}},"official":{"repos":["jaewon-lee-b/lte"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/image-specific-convolutional-kernel","slug":"image-specific-convolutional-kernel","title":"Image-specific Convolutional Kernel Modulation for Single Image Super-resolution","date":"2021-11-16","arxiv_id":"2111.08362","repositories_listed":1,"syntology":null},{"url":"/paper/pixel-level-kernel-estimation-for-blind-super","slug":"pixel-level-kernel-estimation-for-blind-super","title":"Pixel-Level Kernel Estimation for Blind Super-Resolution","date":"2021-11-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/small-or-far-away-exploiting-deep-super","slug":"small-or-far-away-exploiting-deep-super","title":"Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance","date":"2021-11-12","arxiv_id":"2111.06830","repositories_listed":1,"syntology":null},{"url":"/paper/texture-enhanced-light-field-super-resolution","slug":"texture-enhanced-light-field-super-resolution","title":"Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels","date":"2021-11-07","arxiv_id":"2111.04069","repositories_listed":1,"syntology":null},{"url":"/paper/adapool-exponential-adaptive-pooling-for","slug":"adapool-exponential-adaptive-pooling-for","title":"AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling","date":"2021-11-01","arxiv_id":"2111.00772","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-of-multi-resolution-x-ray-micro","slug":"deep-learning-of-multi-resolution-x-ray-micro","title":"Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling","date":"2021-11-01","arxiv_id":"2111.01270","repositories_listed":1,"syntology":null},{"url":"/paper/learning-continuous-representation-of-audio","slug":"learning-continuous-representation-of-audio","title":"Learning Continuous Representation of Audio for Arbitrary Scale Super Resolution","date":"2021-10-30","arxiv_id":"2111.00195","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":1,"n_no_contract":1,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 1 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/learning-continuous-representation-of-audio#ran","syntology_url":"https://syntology.ai/paper/2111.00195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.00195"}},"official":{"repos":["ml-postech/lisa"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-inference-of-latent-dynamics-with-spatio","slug":"deep-inference-of-latent-dynamics-with-spatio","title":"Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time","date":"2021-10-29","arxiv_id":"2111.00070","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":0,"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/deep-inference-of-latent-dynamics-with-spatio#ran","syntology_url":"https://syntology.ai/paper/2111.00070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.00070"}},"official":{"repos":["snel-repo/sbtt-demo"],"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/scale-aware-dynamic-network-for-continuous","slug":"scale-aware-dynamic-network-for-continuous","title":"Scale-Aware Dynamic Network for Continuous-Scale Super-Resolution","date":"2021-10-29","arxiv_id":"2110.15655","repositories_listed":1,"syntology":null},{"url":"/paper/an-arbitrary-scale-super-resolution-approach","slug":"an-arbitrary-scale-super-resolution-approach","title":"An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation","date":"2021-10-27","arxiv_id":"2110.14476","repositories_listed":1,"syntology":null},{"url":"/paper/improving-super-resolution-performance-using","slug":"improving-super-resolution-performance-using","title":"Improving Super-Resolution Performance using Meta-Attention Layers","date":"2021-10-27","arxiv_id":"2110.14638","repositories_listed":1,"syntology":null},{"url":"/paper/localized-super-resolution-for-foreground","slug":"localized-super-resolution-for-foreground","title":"Localized Super Resolution for Foreground Images using U-Net and MR-CNN","date":"2021-10-27","arxiv_id":"2110.14413","repositories_listed":1,"syntology":null},{"url":"/paper/rbsricnn-raw-burst-super-resolution-through","slug":"rbsricnn-raw-burst-super-resolution-through","title":"RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network","date":"2021-10-25","arxiv_id":"2110.13217","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"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 1 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","sample_list":"/paper/rbsricnn-raw-burst-super-resolution-through#ran","syntology_url":"https://syntology.ai/paper/2110.13217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.13217"}},"official":{"repos":["raoumer/rbsricnn"],"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/erqa-edge-restoration-quality-assessment-for","slug":"erqa-edge-restoration-quality-assessment-for","title":"ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution","date":"2021-10-19","arxiv_id":"2110.09992","repositories_listed":1,"syntology":null},{"url":"/paper/scene-text-image-super-resolution-via","slug":"scene-text-image-super-resolution-via","title":"Scene Text Image Super-Resolution via Parallelly Contextual Attention Network","date":"2021-10-17","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/efenet-reference-based-video-super-resolution","slug":"efenet-reference-based-video-super-resolution","title":"EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation","date":"2021-10-15","arxiv_id":"2110.07797","repositories_listed":1,"syntology":null},{"url":"/paper/deep-fusion-prior-for-multi-focus-image-super","slug":"deep-fusion-prior-for-multi-focus-image-super","title":"Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus Imaging","date":"2021-10-12","arxiv_id":"2110.05706","repositories_listed":1,"syntology":null},{"url":"/paper/game-theory-for-adversarial-attacks-and","slug":"game-theory-for-adversarial-attacks-and","title":"Game Theory for Adversarial Attacks and Defenses","date":"2021-10-08","arxiv_id":"2110.06166","repositories_listed":1,"syntology":null},{"url":"/paper/burst-image-restoration-and-enhancement","slug":"burst-image-restoration-and-enhancement","title":"Burst Image Restoration and Enhancement","date":"2021-10-07","arxiv_id":"2110.03680","repositories_listed":1,"syntology":null},{"url":"/paper/gradient-step-denoiser-for-convergent-plug","slug":"gradient-step-denoiser-for-convergent-plug","title":"Gradient Step Denoiser for convergent Plug-and-Play","date":"2021-10-07","arxiv_id":"2110.03220","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/gradient-step-denoiser-for-convergent-plug#ran","syntology_url":"https://syntology.ai/paper/2110.03220","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.03220"}},"official":{"repos":["samuro95/gspnp"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/inter-domain-alignment-for-predicting-high","slug":"inter-domain-alignment-for-predicting-high","title":"Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning","date":"2021-10-06","arxiv_id":"2110.03452","repositories_listed":1,"syntology":null},{"url":"/paper/stairwaygraphnet-for-inter-and-intra-modality","slug":"stairwaygraphnet-for-inter-and-intra-modality","title":"StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis","date":"2021-10-06","arxiv_id":"2110.04279","repositories_listed":1,"syntology":null},{"url":"/paper/from-beginner-to-master-a-survey-for-deep","slug":"from-beginner-to-master-a-survey-for-deep","title":"A Systematic Survey of Deep Learning-based Single-Image Super-Resolution","date":"2021-09-29","arxiv_id":"2109.14335","repositories_listed":1,"syntology":null},{"url":"/paper/tpu-gan-learning-temporal-coherence-from","slug":"tpu-gan-learning-temporal-coherence-from","title":"TPU-GAN: Learning temporal coherence from dynamic point cloud sequences","date":"2021-09-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/structure-preserving-image-super-resolution-1","slug":"structure-preserving-image-super-resolution-1","title":"Structure-Preserving Image Super-Resolution","date":"2021-09-26","arxiv_id":"2109.12530","repositories_listed":1,"syntology":null},{"url":"/paper/towards-representation-learning-for","slug":"towards-representation-learning-for","title":"Towards Representation Learning for Atmospheric Dynamics","date":"2021-09-19","arxiv_id":"2109.09076","repositories_listed":1,"syntology":null},{"url":"/paper/conditionally-parameterized-discretization","slug":"conditionally-parameterized-discretization","title":"Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systems","date":"2021-09-15","arxiv_id":"2109.09510","repositories_listed":1,"syntology":null}],"record_sha256":"e3b796d1c14bb1f5c33ffb64ed95bded50e84aa232d24e088f3980010812b4ea","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}