{"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/denoising/papers/28","list_of":"/task/denoising","task":"Denoising","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":28,"pages_in_order":73,"rows_per_page":100,"rows":[2701,2800],"of":7282,"counts":{"archive_papers_tagged":7282,"with_a_code_link":2838,"where_syntology_ran_a_sample":832,"not_listed_spam_title":0,"listed":7282,"listed_where_code_ran":832,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":720,"every_run_a_failure_of_syntologys_instrument":112,"listed_with_a_run_with_no_instrument_failure":720,"listed_every_run_a_failure_of_syntologys_instrument":112,"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/denoising","prev":"/task/denoising/papers/27","next":"/task/denoising/papers/29","papers":[{"url":"/paper/iterative-residual-cnns-for-burst-photography","slug":"iterative-residual-cnns-for-burst-photography","title":"Iterative Residual CNNs for Burst Photography Applications","date":"2018-11-29","arxiv_id":"1811.12197","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-deep-steins-unbiased-risk","slug":"leveraging-deep-steins-unbiased-risk","title":"Leveraging Deep Stein's Unbiased Risk Estimator for Unsupervised X-ray Denoising","date":"2018-11-29","arxiv_id":"1811.12488","repositories_listed":1,"syntology":null},{"url":"/paper/dense-xunit-networks","slug":"dense-xunit-networks","title":"Dense xUnit Networks","date":"2018-11-27","arxiv_id":"1811.11051","repositories_listed":1,"syntology":null},{"url":"/paper/fully-convolutional-network-with-multi-step","slug":"fully-convolutional-network-with-multi-step","title":"Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing","date":"2018-11-10","arxiv_id":"1811.04323","repositories_listed":1,"syntology":null},{"url":"/paper/can-deep-learning-outperform-modern","slug":"can-deep-learning-outperform-modern","title":"Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?","date":"2018-11-08","arxiv_id":"1811.03691","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":1,"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/can-deep-learning-outperform-modern#ran","syntology_url":"https://syntology.ai/paper/1811.03691","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.03691"}},"official":{"repos":["hmshan/MAP-NN"],"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/dynamic-feature-acquisition-using-denoising","slug":"dynamic-feature-acquisition-using-denoising","title":"Dynamic Feature Acquisition Using Denoising Autoencoders","date":"2018-11-03","arxiv_id":"1811.01249","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/dynamic-feature-acquisition-using-denoising#ran","syntology_url":"https://syntology.ai/paper/1811.01249","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.01249"}},"official":null}},{"url":"/paper/weak-label-supervision-for-monaural-source","slug":"weak-label-supervision-for-monaural-source","title":"Audio Source Separation Using Variational Autoencoders and Weak Class Supervision","date":"2018-10-31","arxiv_id":"1810.13104","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-tutorial-for-denoising","slug":"deep-learning-tutorial-for-denoising","title":"Deep learning for denoising","date":"2018-10-27","arxiv_id":"1810.11614","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-deblurring-for-natural-imaging","slug":"convolutional-deblurring-for-natural-imaging","title":"Convolutional Deblurring for Natural Imaging","date":"2018-10-25","arxiv_id":"1810.10725","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-the-effect-of-residual-and","slug":"investigating-the-effect-of-residual-and","title":"Investigating the effect of residual and highway connections in speech enhancement models","date":"2018-10-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/memc-net-motion-estimation-and-motion","slug":"memc-net-motion-estimation-and-motion","title":"MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement","date":"2018-10-20","arxiv_id":"1810.08768","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":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) · 1 unverified","sample_list":"/paper/memc-net-motion-estimation-and-motion#ran","syntology_url":"https://syntology.ai/paper/1810.08768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.08768"}},"official":null}},{"url":"/paper/memc-net-motion-estimation-and-motion-1","slug":"memc-net-motion-estimation-and-motion-1","title":"MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and Enhancement","date":"2018-10-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/heteroskedastic-pca-algorithm-optimality-and","slug":"heteroskedastic-pca-algorithm-optimality-and","title":"Heteroskedastic PCA: Algorithm, Optimality, and Applications","date":"2018-10-19","arxiv_id":"1810.08316","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-neural-text-simplification","slug":"unsupervised-neural-text-simplification","title":"Unsupervised Neural Text Simplification","date":"2018-10-18","arxiv_id":"1810.07931","repositories_listed":1,"syntology":null},{"url":"/paper/cryo-care-content-aware-image-restoration-for","slug":"cryo-care-content-aware-image-restoration-for","title":"Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Data","date":"2018-10-12","arxiv_id":"1810.05420","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-cardiac-motion-analysis-for","slug":"deep-learning-cardiac-motion-analysis-for","title":"Deep learning cardiac motion analysis for human survival prediction","date":"2018-10-08","arxiv_id":"1810.03382","repositories_listed":1,"syntology":null},{"url":"/paper/an-encoder-decoder-approach-to-the-paradigm","slug":"an-encoder-decoder-approach-to-the-paradigm","title":"An Encoder-Decoder Approach to the Paradigm Cell Filling Problem","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-noise-removal-in-hyperspectral-imagery","slug":"hybrid-noise-removal-in-hyperspectral-imagery","title":"Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network","date":"2018-10-01","arxiv_id":"1810.00495","repositories_listed":1,"syntology":null},{"url":"/paper/low-frequency-adversarial-perturbation","slug":"low-frequency-adversarial-perturbation","title":"Low Frequency Adversarial Perturbation","date":"2018-09-24","arxiv_id":"1809.08758","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/low-frequency-adversarial-perturbation#ran","syntology_url":"https://syntology.ai/paper/1809.08758","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.08758"}},"official":{"repos":["cg563/low-frequency-adversarial"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-brief-review-of-real-world-color-image","slug":"a-brief-review-of-real-world-color-image","title":"A Brief Review of Real-World Color Image Denoising","date":"2018-09-10","arxiv_id":"1809.03298","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-sentence-compression-using","slug":"unsupervised-sentence-compression-using","title":"Unsupervised Sentence Compression using Denoising Auto-Encoders","date":"2018-09-07","arxiv_id":"1809.02669","repositories_listed":1,"syntology":null},{"url":"/paper/connecting-image-denoising-and-high-level","slug":"connecting-image-denoising-and-high-level","title":"Connecting Image Denoising and High-Level Vision Tasks via Deep Learning","date":"2018-09-06","arxiv_id":"1809.01826","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-single-channel-dereverberation-and","slug":"real-time-single-channel-dereverberation-and","title":"Real-time Single-channel Dereverberation and Separation with Time-domainAudio Separation Network","date":"2018-09-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-relational-networks-for-group","slug":"hierarchical-relational-networks-for-group","title":"Hierarchical Relational Networks for Group Activity Recognition and Retrieval","date":"2018-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/autoencoders-kernels-and-multilayer","slug":"autoencoders-kernels-and-multilayer","title":"Autoencoders, Kernels, and Multilayer Perceptrons for Electron Micrograph Restoration and Compression","date":"2018-08-29","arxiv_id":"1808.09916","repositories_listed":1,"syntology":null},{"url":"/paper/class-aware-fully-convolutional-gaussian-and","slug":"class-aware-fully-convolutional-gaussian-and","title":"Class-Aware Fully-Convolutional Gaussian and Poisson Denoising","date":"2018-08-20","arxiv_id":"1808.06562","repositories_listed":1,"syntology":null},{"url":"/paper/robust-compressive-phase-retrieval-via-deep","slug":"robust-compressive-phase-retrieval-via-deep","title":"Robust Compressive Phase Retrieval via Deep Generative Priors","date":"2018-08-17","arxiv_id":"1808.05854","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-of-3-d-magnetic-resonance-images","slug":"denoising-of-3-d-magnetic-resonance-images","title":"Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial Network","date":"2018-08-12","arxiv_id":"1808.03941","repositories_listed":1,"syntology":null},{"url":"/paper/deep-end-to-end-fingerprint-denoising-and","slug":"deep-end-to-end-fingerprint-denoising-and","title":"Deep End-to-end Fingerprint Denoising and Inpainting","date":"2018-07-31","arxiv_id":"1807.11888","repositories_listed":1,"syntology":null},{"url":"/paper/deep-graph-laplacian-regularization-for","slug":"deep-graph-laplacian-regularization-for","title":"Deep Graph Laplacian Regularization for Robust Denoising of Real Images","date":"2018-07-31","arxiv_id":"1807.11637","repositories_listed":1,"syntology":null},{"url":"/paper/using-feature-grouping-as-a-stochastic","slug":"using-feature-grouping-as-a-stochastic","title":"Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data","date":"2018-07-31","arxiv_id":"1807.11718","repositories_listed":1,"syntology":null},{"url":"/paper/deep-recurrent-neural-networks-for-ecg-signal","slug":"deep-recurrent-neural-networks-for-ecg-signal","title":"Deep Recurrent Neural Networks for ECG Signal Denoising","date":"2018-07-30","arxiv_id":"1807.11551","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-recurrent-neural-networks-for-ecg-signal#ran","syntology_url":"https://syntology.ai/paper/1807.11551","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.11551"}},"official":null}},{"url":"/paper/u-finger-multi-scale-dilated-convolutional","slug":"u-finger-multi-scale-dilated-convolutional","title":"U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting","date":"2018-07-29","arxiv_id":"1807.10993","repositories_listed":1,"syntology":null},{"url":"/paper/face-de-spoofing-anti-spoofing-via-noise","slug":"face-de-spoofing-anti-spoofing-via-noise","title":"Face De-Spoofing: Anti-Spoofing via Noise Modeling","date":"2018-07-26","arxiv_id":"1807.09968","repositories_listed":1,"syntology":null},{"url":"/paper/decouple-learning-for-parameterized-image","slug":"decouple-learning-for-parameterized-image","title":"Decouple Learning for Parameterized Image Operators","date":"2018-07-21","arxiv_id":"1807.08186","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/decouple-learning-for-parameterized-image#ran","syntology_url":"https://syntology.ai/paper/1807.08186","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.08186"}},"official":{"repos":["fqnchina/DecoupleLearning"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/penalized-matrix-decomposition-for-denoising","slug":"penalized-matrix-decomposition-for-denoising","title":"Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data","date":"2018-07-17","arxiv_id":"1807.06203","repositories_listed":1,"syntology":null},{"url":"/paper/combining-a-context-aware-neural-network-with","slug":"combining-a-context-aware-neural-network-with","title":"Combining a Context Aware Neural Network with a Denoising Autoencoder for Measuring String Similarities","date":"2018-07-16","arxiv_id":"1807.06414","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-residual-network-for-deep-joint","slug":"iterative-residual-network-for-deep-joint","title":"Iterative Joint Image Demosaicking and Denoising using a Residual Denoising Network","date":"2018-07-16","arxiv_id":"1807.06403","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"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 0 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","sample_list":"/paper/iterative-residual-network-for-deep-joint#ran","syntology_url":"https://syntology.ai/paper/1807.06403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.06403"}},"official":{"repos":["cig-skoltech/deep_demosaick"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/from-rank-estimation-to-rank-approximation","slug":"from-rank-estimation-to-rank-approximation","title":"From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration","date":"2018-07-06","arxiv_id":"1807.02504","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-distantly-supervised-open-domain","slug":"denoising-distantly-supervised-open-domain","title":"Denoising Distantly Supervised Open-Domain Question Answering","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sparse-geometric-representation-through-local","slug":"sparse-geometric-representation-through-local","title":"Sparse Geometric Representation Through Local Shape Probing","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/coupled-dictionary-learning-for-multi","slug":"coupled-dictionary-learning-for-multi","title":"Coupled Dictionary Learning for Multi-contrast MRI Reconstruction","date":"2018-06-26","arxiv_id":"1806.09930","repositories_listed":1,"syntology":null},{"url":"/paper/cycle-consistent-adversarial-denoising","slug":"cycle-consistent-adversarial-denoising","title":"Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography","date":"2018-06-26","arxiv_id":"1806.09748","repositories_listed":1,"syntology":null},{"url":"/paper/learning-dynamics-of-linear-denoising","slug":"learning-dynamics-of-linear-denoising","title":"Learning Dynamics of Linear Denoising Autoencoders","date":"2018-06-14","arxiv_id":"1806.05413","repositories_listed":1,"syntology":{"n":19,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/learning-dynamics-of-linear-denoising#ran","syntology_url":"https://syntology.ai/paper/1806.05413","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.05413"}},"official":{"repos":["arnupretorius/lindaedynamics_icml2018"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/identifying-recurring-patterns-with-deep","slug":"identifying-recurring-patterns-with-deep","title":"Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising","date":"2018-06-13","arxiv_id":"1806.05229","repositories_listed":1,"syntology":null},{"url":"/paper/non-local-recurrent-network-for-image","slug":"non-local-recurrent-network-for-image","title":"Non-Local Recurrent Network for Image Restoration","date":"2018-06-07","arxiv_id":"1806.02919","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/non-local-recurrent-network-for-image#ran","syntology_url":"https://syntology.ai/paper/1806.02919","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02919"}},"official":{"repos":["Ding-Liu/NLRN"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/regularization-by-denoising-clarifications","slug":"regularization-by-denoising-clarifications","title":"Regularization by Denoising: Clarifications and New Interpretations","date":"2018-06-06","arxiv_id":"1806.02296","repositories_listed":1,"syntology":null},{"url":"/paper/coconet-a-deep-neural-network-for-mapping","slug":"coconet-a-deep-neural-network-for-mapping","title":"CocoNet: A deep neural network for mapping pixel coordinates to color values","date":"2018-05-29","arxiv_id":"1805.11357","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-learning-with-steins-unbiased","slug":"unsupervised-learning-with-steins-unbiased","title":"Unsupervised Learning with Stein's Unbiased Risk Estimator","date":"2018-05-26","arxiv_id":"1805.10531","repositories_listed":1,"syntology":null},{"url":"/paper/accelerated-gossip-in-networks-of-given","slug":"accelerated-gossip-in-networks-of-given","title":"Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial Iterations","date":"2018-05-22","arxiv_id":"1805.08531","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/accelerated-gossip-in-networks-of-given#ran","syntology_url":"https://syntology.ai/paper/1805.08531","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.08531"}},"official":{"repos":["raphael-berthier/jacobi-polynomial-iterations"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-energy-estimator-networks","slug":"deep-energy-estimator-networks","title":"Deep Energy Estimator Networks","date":"2018-05-21","arxiv_id":"1805.08306","repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-gaussian-mechanism-for","slug":"improving-the-gaussian-mechanism-for","title":"Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising","date":"2018-05-16","arxiv_id":"1805.06530","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/improving-the-gaussian-mechanism-for#ran","syntology_url":"https://syntology.ai/paper/1805.06530","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.06530"}},"official":{"repos":["BorjaBalle/analytic-gaussian-mechanism"],"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/k-space-deep-learning-for-accelerated-mri","slug":"k-space-deep-learning-for-accelerated-mri","title":"k-Space Deep Learning for Accelerated MRI","date":"2018-05-10","arxiv_id":"1805.03779","repositories_listed":1,"syntology":null},{"url":"/paper/new-techniques-for-preserving-global","slug":"new-techniques-for-preserving-global","title":"New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution","date":"2018-05-09","arxiv_id":"1805.03383","repositories_listed":1,"syntology":null},{"url":"/paper/moire-photo-restoration-using-multiresolution","slug":"moire-photo-restoration-using-multiresolution","title":"Moiré Photo Restoration Using Multiresolution Convolutional Neural Networks","date":"2018-05-08","arxiv_id":"1805.02996","repositories_listed":1,"syntology":null},{"url":"/paper/acceleration-of-red-via-vector-extrapolation","slug":"acceleration-of-red-via-vector-extrapolation","title":"Acceleration of RED via Vector Extrapolation","date":"2018-05-06","arxiv_id":"1805.02158","repositories_listed":1,"syntology":null},{"url":"/paper/joint-enhancement-and-denoising-method-via","slug":"joint-enhancement-and-denoising-method-via","title":"Joint Enhancement and Denoising Method via Sequential Decomposition","date":"2018-04-23","arxiv_id":"1804.08468","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-natural-language-generation-with","slug":"unsupervised-natural-language-generation-with","title":"Unsupervised Natural Language Generation with Denoising Autoencoders","date":"2018-04-21","arxiv_id":"1804.07899","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-natural-language-generation-with#ran","syntology_url":"https://syntology.ai/paper/1804.07899","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.07899"}},"official":null}},{"url":"/paper/deep-neural-networks-motivated-by-partial","slug":"deep-neural-networks-motivated-by-partial","title":"Deep Neural Networks Motivated by Partial Differential Equations","date":"2018-04-12","arxiv_id":"1804.04272","repositories_listed":1,"syntology":null},{"url":"/paper/simultaneous-fidelity-and-regularization","slug":"simultaneous-fidelity-and-regularization","title":"Simultaneous Fidelity and Regularization Learning for Image Restoration","date":"2018-04-12","arxiv_id":"1804.04522","repositories_listed":1,"syntology":null},{"url":"/paper/deepasl-kinetic-model-incorporated-loss-for","slug":"deepasl-kinetic-model-incorporated-loss-for","title":"DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual Learning","date":"2018-04-08","arxiv_id":"1804.02755","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-quantile-sparse-image-aquasi-prior","slug":"adaptive-quantile-sparse-image-aquasi-prior","title":"Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems","date":"2018-04-06","arxiv_id":"1804.02152","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-first-order-algorithms-for-adaptive","slug":"efficient-first-order-algorithms-for-adaptive","title":"Efficient First-Order Algorithms for Adaptive Signal Denoising","date":"2018-03-29","arxiv_id":"1803.11262","repositories_listed":1,"syntology":null},{"url":"/paper/context-aware-deep-feature-compression-for","slug":"context-aware-deep-feature-compression-for","title":"Context-aware Deep Feature Compression for High-speed Visual Tracking","date":"2018-03-28","arxiv_id":"1803.10537","repositories_listed":1,"syntology":null},{"url":"/paper/deep-faster-detection-of-faint-edges-in-noisy","slug":"deep-faster-detection-of-faint-edges-in-noisy","title":"Multi-scale Processing of Noisy Images using Edge Preservation Losses","date":"2018-03-26","arxiv_id":"1803.09420","repositories_listed":1,"syntology":null},{"url":"/paper/lifting-layers-analysis-and-applications","slug":"lifting-layers-analysis-and-applications","title":"Lifting Layers: Analysis and Applications","date":"2018-03-23","arxiv_id":"1803.08660","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-adversarial-examples","slug":"semantic-adversarial-examples","title":"Semantic Adversarial Examples","date":"2018-03-16","arxiv_id":"1804.00499","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/semantic-adversarial-examples#ran","syntology_url":"https://syntology.ai/paper/1804.00499","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00499"}},"official":null}},{"url":"/paper/deep-image-demosaicking-using-a-cascade-of","slug":"deep-image-demosaicking-using-a-cascade-of","title":"Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks","date":"2018-03-14","arxiv_id":"1803.05215","repositories_listed":1,"syntology":null},{"url":"/paper/nonlocality-reinforced-convolutional-neural","slug":"nonlocality-reinforced-convolutional-neural","title":"Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising","date":"2018-03-06","arxiv_id":"1803.02112","repositories_listed":1,"syntology":null},{"url":"/paper/l_p-norm-constrained-coding-with-frank-wolfe","slug":"l_p-norm-constrained-coding-with-frank-wolfe","title":"Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding","date":"2018-02-28","arxiv_id":"1802.10252","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-prior-driven-deep-neural-network","slug":"denoising-prior-driven-deep-neural-network","title":"Denoising Prior Driven Deep Neural Network for Image Restoration","date":"2018-01-21","arxiv_id":"1801.06756","repositories_listed":1,"syntology":null},{"url":"/paper/dendritic-error-backpropagation-in-deep","slug":"dendritic-error-backpropagation-in-deep","title":"Dendritic error backpropagation in deep cortical microcircuits","date":"2017-12-30","arxiv_id":"1801.00062","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-of-3d-magnetic-resonance-images","slug":"denoising-of-3d-magnetic-resonance-images","title":"Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network","date":"2017-12-23","arxiv_id":"1712.08726","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-learning-of-stochastic-dynamic","slug":"sparse-learning-of-stochastic-dynamic","title":"Sparse learning of stochastic dynamic equations","date":"2017-12-06","arxiv_id":"1712.02432","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sparse-learning-of-stochastic-dynamic#ran","syntology_url":"https://syntology.ai/paper/1712.02432","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.02432"}},"official":null}},{"url":"/paper/xunit-learning-a-spatial-activation-function","slug":"xunit-learning-a-spatial-activation-function","title":"xUnit: Learning a Spatial Activation Function for Efficient Image Restoration","date":"2017-11-17","arxiv_id":"1711.06445","repositories_listed":1,"syntology":null},{"url":"/paper/improving-hypernymy-extraction-with","slug":"improving-hypernymy-extraction-with","title":"Improving Hypernymy Extraction with Distributional Semantic Classes","date":"2017-11-08","arxiv_id":"1711.02918","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-autoencoder-a-deep-learning","slug":"multimodal-autoencoder-a-deep-learning","title":"Multimodal Autoencoder: A Deep Learning Approach to Filling In Missing Sensor Data and Enabling Better Mood Prediction","date":"2017-10-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/linear-time-algorithm-in-bayesian-image","slug":"linear-time-algorithm-in-bayesian-image","title":"Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field","date":"2017-10-20","arxiv_id":"1710.07393","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-pet-image-reconstruction-using","slug":"iterative-pet-image-reconstruction-using","title":"Iterative PET Image Reconstruction Using Convolutional Neural Network Representation","date":"2017-10-09","arxiv_id":"1710.03344","repositories_listed":1,"syntology":null},{"url":"/paper/isotropic-and-steerable-wavelets-in-n","slug":"isotropic-and-steerable-wavelets-in-n","title":"Isotropic and Steerable Wavelets in N Dimensions. A multiresolution analysis framework for ITK","date":"2017-10-03","arxiv_id":"1710.01103","repositories_listed":1,"syntology":null},{"url":"/paper/vidosat-high-dimensional-sparsifying","slug":"vidosat-high-dimensional-sparsifying","title":"VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising","date":"2017-10-03","arxiv_id":"1710.00947","repositories_listed":1,"syntology":null},{"url":"/paper/joint-adaptive-sparsity-and-low-rankness-on","slug":"joint-adaptive-sparsity-and-low-rankness-on","title":"Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising","date":"2017-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-principal-component-analysis","slug":"contrastive-principal-component-analysis","title":"Contrastive Principal Component Analysis","date":"2017-09-20","arxiv_id":"1709.06716","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-and-unsupervised-speech","slug":"supervised-and-unsupervised-speech","title":"Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization","date":"2017-09-15","arxiv_id":"1709.05362","repositories_listed":1,"syntology":null},{"url":"/paper/deep-mean-shift-priors-for-image-restoration","slug":"deep-mean-shift-priors-for-image-restoration","title":"Deep Mean-Shift Priors for Image Restoration","date":"2017-09-12","arxiv_id":"1709.03749","repositories_listed":1,"syntology":null},{"url":"/paper/vigan-missing-view-imputation-with-generative","slug":"vigan-missing-view-imputation-with-generative","title":"VIGAN: Missing View Imputation with Generative Adversarial Networks","date":"2017-08-22","arxiv_id":"1708.06724","repositories_listed":1,"syntology":null},{"url":"/paper/anomaly-detection-with-robust-deep","slug":"anomaly-detection-with-robust-deep","title":"Anomaly Detection with Robust Deep Autoencoders","date":"2017-08-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/from-patches-to-images-a-nonparametric","slug":"from-patches-to-images-a-nonparametric","title":"From Patches to Images: A Nonparametric Generative Model","date":"2017-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-convolutional-framelet-denosing-for-low","slug":"deep-convolutional-framelet-denosing-for-low","title":"Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network","date":"2017-07-31","arxiv_id":"1707.09938","repositories_listed":1,"syntology":null},{"url":"/paper/learning-pixel-distribution-prior-with-wider","slug":"learning-pixel-distribution-prior-with-wider","title":"Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising","date":"2017-07-28","arxiv_id":"1707.09135","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-dictionary-learning","slug":"convolutional-dictionary-learning","title":"Convolutional Dictionary Learning: Acceleration and Convergence","date":"2017-07-03","arxiv_id":"1707.00389","repositories_listed":1,"syntology":null},{"url":"/paper/gated-orthogonal-recurrent-units-on-learning","slug":"gated-orthogonal-recurrent-units-on-learning","title":"Gated Orthogonal Recurrent Units: On Learning to Forget","date":"2017-06-08","arxiv_id":"1706.02761","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-principled-score-estimation","slug":"efficient-and-principled-score-estimation","title":"Efficient and principled score estimation with Nyström kernel exponential families","date":"2017-05-23","arxiv_id":"1705.08360","repositories_listed":1,"syntology":null},{"url":"/paper/image-segmentation-by-iterative-inference","slug":"image-segmentation-by-iterative-inference","title":"Image Segmentation by Iterative Inference from Conditional Score Estimation","date":"2017-05-21","arxiv_id":"1705.07450","repositories_listed":1,"syntology":null},{"url":"/paper/a-design-methodology-for-efficient","slug":"a-design-methodology-for-efficient","title":"A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA","date":"2017-05-07","arxiv_id":"1705.02583","repositories_listed":1,"syntology":null},{"url":"/paper/learned-d-amp-principled-neural-network-based","slug":"learned-d-amp-principled-neural-network-based","title":"Learned D-AMP: Principled Neural Network based Compressive Image Recovery","date":"2017-04-21","arxiv_id":"1704.06625","repositories_listed":1,"syntology":null},{"url":"/paper/learning-proximal-operators-using-denoising","slug":"learning-proximal-operators-using-denoising","title":"Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems","date":"2017-04-11","arxiv_id":"1704.03488","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":1,"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/learning-proximal-operators-using-denoising#ran","syntology_url":"https://syntology.ai/paper/1704.03488","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.03488"}},"official":{"repos":["tum-vision/learn_prox_ops"],"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/robust-kronecker-decomposable-component","slug":"robust-kronecker-decomposable-component","title":"Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling","date":"2017-03-22","arxiv_id":"1703.07886","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-generate-samples-from-noise","slug":"learning-to-generate-samples-from-noise","title":"Learning to Generate Samples from Noise through Infusion Training","date":"2017-03-20","arxiv_id":"1703.06975","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-adversarial-autoencoders","slug":"denoising-adversarial-autoencoders","title":"Denoising Adversarial Autoencoders","date":"2017-03-03","arxiv_id":"1703.01220","repositories_listed":1,"syntology":null}],"record_sha256":"e68a87fd0c70ddecce2a6b41cdaf82f1a991b8318aed5d8de154435f0e1c3459","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}