{"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-reconstruction/papers/5","list_of":"/task/image-reconstruction","task":"Image Reconstruction","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":22,"rows_per_page":100,"rows":[401,500],"of":2143,"counts":{"archive_papers_tagged":2143,"with_a_code_link":712,"where_syntology_ran_a_sample":135,"not_listed_spam_title":0,"listed":2143,"listed_where_code_ran":135,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":114,"every_run_a_failure_of_syntologys_instrument":21,"listed_with_a_run_with_no_instrument_failure":114,"listed_every_run_a_failure_of_syntologys_instrument":21,"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-reconstruction","prev":"/task/image-reconstruction/papers/4","next":"/task/image-reconstruction/papers/6","papers":[{"url":"/paper/data-consistent-deep-rigid-mri-motion","slug":"data-consistent-deep-rigid-mri-motion","title":"Data Consistent Deep Rigid MRI Motion Correction","date":"2023-01-25","arxiv_id":"2301.10365","repositories_listed":1,"syntology":null},{"url":"/paper/a-simple-adaptive-unfolding-network-for","slug":"a-simple-adaptive-unfolding-network-for","title":"A Simple Adaptive Unfolding Network for Hyperspectral Image Reconstruction","date":"2023-01-24","arxiv_id":"2301.10208","repositories_listed":1,"syntology":null},{"url":"/paper/position-regression-for-unsupervised-anomaly","slug":"position-regression-for-unsupervised-anomaly","title":"Position Regression for Unsupervised Anomaly Detection","date":"2023-01-19","arxiv_id":"2301.08064","repositories_listed":1,"syntology":null},{"url":"/paper/a-synthetic-hyperspectral-array-video","slug":"a-synthetic-hyperspectral-array-video","title":"Synthetic Hyperspectral Array Video Database with Applications to Cross-Spectral Reconstruction and Hyperspectral Video Coding","date":"2023-01-18","arxiv_id":"2301.07551","repositories_listed":1,"syntology":null},{"url":"/paper/targeted-image-reconstruction-by-sampling-pre","slug":"targeted-image-reconstruction-by-sampling-pre","title":"Targeted Image Reconstruction by Sampling Pre-trained Diffusion Model","date":"2023-01-18","arxiv_id":"2301.07557","repositories_listed":1,"syntology":null},{"url":"/paper/high-resolution-cloud-removal-with-multi","slug":"high-resolution-cloud-removal-with-multi","title":"Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark","date":"2023-01-09","arxiv_id":"2301.03432","repositories_listed":1,"syntology":null},{"url":"/paper/context-aware-pretraining-for-efficient-blind","slug":"context-aware-pretraining-for-efficient-blind","title":"Context-Aware Pretraining for Efficient Blind Image Decomposition","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/high-resolution-image-reconstruction-with","slug":"high-resolution-image-reconstruction-with","title":"High-Resolution Image Reconstruction With Latent Diffusion Models From Human Brain Activity","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/styleres-transforming-the-residuals-for-real","slug":"styleres-transforming-the-residuals-for-real","title":"StyleRes: Transforming the Residuals for Real Image Editing with StyleGAN","date":"2022-12-29","arxiv_id":"2212.14359","repositories_listed":1,"syntology":null},{"url":"/paper/continual-learning-approaches-for-anomaly","slug":"continual-learning-approaches-for-anomaly","title":"Continual Learning Approaches for Anomaly Detection","date":"2022-12-21","arxiv_id":"2212.11192","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-brain-decoding-from-fmri-to","slug":"semantic-brain-decoding-from-fmri-to","title":"Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli","date":"2022-12-13","arxiv_id":"2212.06726","repositories_listed":1,"syntology":null},{"url":"/paper/latent-graph-representations-for-critical","slug":"latent-graph-representations-for-critical","title":"Latent Graph Representations for Critical View of Safety Assessment","date":"2022-12-08","arxiv_id":"2212.04155","repositories_listed":1,"syntology":null},{"url":"/paper/generative-modeling-in-sinogram-domain-for","slug":"generative-modeling-in-sinogram-domain-for","title":"Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction","date":"2022-11-25","arxiv_id":"2211.13926","repositories_listed":1,"syntology":null},{"url":"/paper/maeday-mae-for-few-and-zero-shot-anomaly","slug":"maeday-mae-for-few-and-zero-shot-anomaly","title":"MAEDAY: MAE for few and zero shot AnomalY-Detection","date":"2022-11-25","arxiv_id":"2211.14307","repositories_listed":1,"syntology":null},{"url":"/paper/pip-positional-encoding-image-prior","slug":"pip-positional-encoding-image-prior","title":"PIP: Positional-encoding Image Prior","date":"2022-11-25","arxiv_id":"2211.14298","repositories_listed":1,"syntology":null},{"url":"/paper/solving-3d-inverse-problems-using-pre-trained","slug":"solving-3d-inverse-problems-using-pre-trained","title":"Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models","date":"2022-11-19","arxiv_id":"2211.10655","repositories_listed":1,"syntology":null},{"url":"/paper/differentiable-uncalibrated-imaging","slug":"differentiable-uncalibrated-imaging","title":"Differentiable Uncalibrated Imaging","date":"2022-11-18","arxiv_id":"2211.10525","repositories_listed":1,"syntology":null},{"url":"/paper/accelerated-motion-correction-for-mri-using","slug":"accelerated-motion-correction-for-mri-using","title":"Accelerated Motion Correction with Deep Generative Diffusion Models","date":"2022-11-01","arxiv_id":"2211.00199","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/accelerated-motion-correction-for-mri-using#ran","syntology_url":"https://syntology.ai/paper/2211.00199","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.00199"}},"official":{"repos":["utcsilab/motion_score_mri"],"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"]}}},{"url":"/paper/masked-vision-language-transformer-in-fashion","slug":"masked-vision-language-transformer-in-fashion","title":"Masked Vision-Language Transformer in Fashion","date":"2022-10-27","arxiv_id":"2210.15110","repositories_listed":1,"syntology":null},{"url":"/paper/exposure-aware-dynamic-weighted-learning-for","slug":"exposure-aware-dynamic-weighted-learning-for","title":"Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging","date":"2022-10-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/combining-band-frequency-separation-and-deep","slug":"combining-band-frequency-separation-and-deep","title":"Combining band-frequency separation and deep neural networks for optoacoustic imaging","date":"2022-10-14","arxiv_id":"2210.08099","repositories_listed":1,"syntology":null},{"url":"/paper/3d-gan-inversion-with-pose-optimization","slug":"3d-gan-inversion-with-pose-optimization","title":"3D GAN Inversion with Pose Optimization","date":"2022-10-13","arxiv_id":"2210.07301","repositories_listed":1,"syntology":null},{"url":"/paper/trap-and-replace-defending-backdoor-attacks","slug":"trap-and-replace-defending-backdoor-attacks","title":"Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork","date":"2022-10-12","arxiv_id":"2210.06428","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/trap-and-replace-defending-backdoor-attacks#ran","syntology_url":"https://syntology.ai/paper/2210.06428","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06428"}},"official":{"repos":["vita-group/trap-and-replace-backdoor-defense"],"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/automated-segmentation-and-morphological","slug":"automated-segmentation-and-morphological","title":"Automated segmentation and morphological characterization of placental histology images based on a single labeled image","date":"2022-10-07","arxiv_id":"2210.03566","repositories_listed":1,"syntology":null},{"url":"/paper/image-compressed-sensing-with-multi-scale","slug":"image-compressed-sensing-with-multi-scale","title":"Image Compressed Sensing with Multi-scale Dilated Convolutional Neural Network","date":"2022-09-28","arxiv_id":"2209.13761","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-laws-for-deep-learning-based-image","slug":"scaling-laws-for-deep-learning-based-image","title":"Scaling Laws For Deep Learning Based Image Reconstruction","date":"2022-09-27","arxiv_id":"2209.13435","repositories_listed":1,"syntology":null},{"url":"/paper/s-2-transformer-for-mask-aware-hyperspectral","slug":"s-2-transformer-for-mask-aware-hyperspectral","title":"S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction","date":"2022-09-24","arxiv_id":"2209.12075","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-coordinate-projection-network","slug":"self-supervised-coordinate-projection-network","title":"Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography","date":"2022-09-12","arxiv_id":"2209.05483","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-for-non-melanoma-skin","slug":"representation-learning-for-non-melanoma-skin","title":"Representation Learning for Non-Melanoma Skin Cancer using a Latent Autoencoder","date":"2022-09-05","arxiv_id":"2209.01779","repositories_listed":1,"syntology":null},{"url":"/paper/joint-demosaicing-and-fusion-of","slug":"joint-demosaicing-and-fusion-of","title":"Joint demosaicing and fusion of multiresolution coded acquisitions: A unified image formation and reconstruction method","date":"2022-09-03","arxiv_id":"2209.01455","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-communications-with-discrete-time","slug":"semantic-communications-with-discrete-time","title":"Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective","date":"2022-08-17","arxiv_id":"2208.08342","repositories_listed":1,"syntology":null},{"url":"/paper/k-unn-k-space-interpolation-with-untrained","slug":"k-unn-k-space-interpolation-with-untrained","title":"K-UNN: k-Space Interpolation With Untrained Neural Network","date":"2022-08-11","arxiv_id":"2208.05827","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-residual-learning-based-vector","slug":"hierarchical-residual-learning-based-vector","title":"Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and Generation","date":"2022-08-09","arxiv_id":"2208.04554","repositories_listed":1,"syntology":null},{"url":"/paper/npb-rec-non-parametric-assessment-of","slug":"npb-rec-non-parametric-assessment-of","title":"NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Data","date":"2022-08-08","arxiv_id":"2208.03966","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-robustness-of-mr-image","slug":"adversarial-robustness-of-mr-image","title":"Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations","date":"2022-08-05","arxiv_id":"2208.03161","repositories_listed":1,"syntology":null},{"url":"/paper/speckle2speckle-unsupervised-learning-of","slug":"speckle2speckle-unsupervised-learning-of","title":"Speckle2Speckle: Unsupervised Learning of Ultrasound Speckle Filtering Without Clean Data","date":"2022-07-31","arxiv_id":"2208.00402","repositories_listed":1,"syntology":null},{"url":"/paper/seeing-far-in-the-dark-with-patterned-flash","slug":"seeing-far-in-the-dark-with-patterned-flash","title":"Seeing Far in the Dark with Patterned Flash","date":"2022-07-25","arxiv_id":"2207.12570","repositories_listed":1,"syntology":null},{"url":"/paper/kunet-imaging-knowledge-inspired-single-hdr","slug":"kunet-imaging-knowledge-inspired-single-hdr","title":"KUNet: Imaging Knowledge-Inspired Single HDR Image Reconstruction","date":"2022-07-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dc-shadownet-single-image-hard-and-soft-1","slug":"dc-shadownet-single-image-hard-and-soft-1","title":"DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network","date":"2022-07-21","arxiv_id":"2207.10434","repositories_listed":1,"syntology":null},{"url":"/paper/the-brain-inspired-decoder-for-natural-visual","slug":"the-brain-inspired-decoder-for-natural-visual","title":"The Brain-Inspired Decoder for Natural Visual Image Reconstruction","date":"2022-07-18","arxiv_id":"2207.08591","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-construction-of-averaged-deep","slug":"on-the-construction-of-averaged-deep","title":"Averaged Deep Denoisers for Image Regularization","date":"2022-07-15","arxiv_id":"2207.07321","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-latent-structure-for-multi-modal","slug":"hierarchical-latent-structure-for-multi-modal","title":"Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting","date":"2022-07-11","arxiv_id":"2207.04624","repositories_listed":1,"syntology":null},{"url":"/paper/deepps2-revisiting-photometric-stereo-using","slug":"deepps2-revisiting-photometric-stereo-using","title":"DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images","date":"2022-07-05","arxiv_id":"2207.02025","repositories_listed":1,"syntology":null},{"url":"/paper/global-sensing-and-measurements-reuse-for-1","slug":"global-sensing-and-measurements-reuse-for-1","title":"Global Sensing and Measurements Reuse for Image Compressed Sensing","date":"2022-06-23","arxiv_id":"2206.11629","repositories_listed":1,"syntology":null},{"url":"/paper/oadat-experimental-and-synthetic-clinical","slug":"oadat-experimental-and-synthetic-clinical","title":"OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing","date":"2022-06-17","arxiv_id":"2206.08612","repositories_listed":1,"syntology":null},{"url":"/paper/pythae-unifying-generative-autoencoders-in","slug":"pythae-unifying-generative-autoencoders-in","title":"Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case","date":"2022-06-16","arxiv_id":"2206.08309","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-dictionary-learning-by-end-to","slug":"convolutional-dictionary-learning-by-end-to","title":"Convolutional Dictionary Learning by End-To-End Training of Iterative Neural Networks","date":"2022-06-09","arxiv_id":"2206.04447","repositories_listed":1,"syntology":null},{"url":"/paper/cross-domain-heterogeneous-residual-network","slug":"cross-domain-heterogeneous-residual-network","title":"Cross-domain heterogeneous residual network for single image super-resolution","date":"2022-05-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/siprnet-end-to-end-learning-for-single-shot","slug":"siprnet-end-to-end-learning-for-single-shot","title":"SiSPRNet: End-to-End Learning for Single-Shot Phase Retrieval","date":"2022-05-23","arxiv_id":"2205.11434","repositories_listed":1,"syntology":null},{"url":"/paper/degradation-aware-unfolding-half-shuffle","slug":"degradation-aware-unfolding-half-shuffle","title":"Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging","date":"2022-05-20","arxiv_id":"2205.10102","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/degradation-aware-unfolding-half-shuffle#ran","syntology_url":"https://syntology.ai/paper/2205.10102","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10102"}},"official":{"repos":["caiyuanhao1998/MST"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/nonlinear-motion-separation-via-untrained","slug":"nonlinear-motion-separation-via-untrained","title":"Latent-space disentanglement with untrained generator networks for the isolation of different motion types in video data","date":"2022-05-20","arxiv_id":"2205.10367","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-deep-learning-mri","slug":"self-supervised-deep-learning-mri","title":"A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise","date":"2022-05-20","arxiv_id":"2205.10278","repositories_listed":1,"syntology":null},{"url":"/paper/a-framework-for-event-based-computer-vision","slug":"a-framework-for-event-based-computer-vision","title":"A Framework for Event-based Computer Vision on a Mobile Device","date":"2022-05-13","arxiv_id":"2205.06836","repositories_listed":1,"syntology":null},{"url":"/paper/student-collaboration-improves-self","slug":"student-collaboration-improves-self","title":"Multiplexed Immunofluorescence Brain Image Analysis Using Self-Supervised Dual-Loss Adaptive Masked Autoencoder","date":"2022-05-10","arxiv_id":"2205.05194","repositories_listed":1,"syntology":null},{"url":"/paper/lightweight-bimodal-network-for-single-image","slug":"lightweight-bimodal-network-for-single-image","title":"Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer","date":"2022-04-28","arxiv_id":"2204.13286","repositories_listed":1,"syntology":null},{"url":"/paper/test-time-training-can-close-the-natural","slug":"test-time-training-can-close-the-natural","title":"Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing","date":"2022-04-14","arxiv_id":"2204.07204","repositories_listed":1,"syntology":null},{"url":"/paper/a-post-processing-tool-and-feasibility-study","slug":"a-post-processing-tool-and-feasibility-study","title":"A Post-Processing Tool and Feasibility Study for Three-Dimensional Imaging with Electrical Impedance Tomography During Deep Brain Stimulation Surgery","date":"2022-04-11","arxiv_id":"2204.05201","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-change-detection-based-on-image","slug":"unsupervised-change-detection-based-on-image","title":"Unsupervised Change Detection Based on Image Reconstruction Loss","date":"2022-04-04","arxiv_id":"2204.01200","repositories_listed":1,"syntology":null},{"url":"/paper/comparison-of-convolutional-neural-networks","slug":"comparison-of-convolutional-neural-networks","title":"Comparison of convolutional neural networks for cloudy optical images reconstruction from single or multitemporal joint SAR and optical images","date":"2022-04-01","arxiv_id":"2204.00424","repositories_listed":1,"syntology":null},{"url":"/paper/monitored-distillation-for-positive-congruent","slug":"monitored-distillation-for-positive-congruent","title":"Monitored Distillation for Positive Congruent Depth Completion","date":"2022-03-30","arxiv_id":"2203.16034","repositories_listed":1,"syntology":null},{"url":"/paper/how-to-robustify-black-box-ml-models-a-zeroth-1","slug":"how-to-robustify-black-box-ml-models-a-zeroth-1","title":"How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective","date":"2022-03-27","arxiv_id":"2203.14195","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/how-to-robustify-black-box-ml-models-a-zeroth-1#ran","syntology_url":"https://syntology.ai/paper/2203.14195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14195"}},"official":{"repos":["damon-demon/black-box-defense"],"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/a-deep-learning-framework-to-reconstruct-face","slug":"a-deep-learning-framework-to-reconstruct-face","title":"A Deep Learning Framework to Reconstruct Face under Mask","date":"2022-03-23","arxiv_id":"2203.12482","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-anomaly-detection-in-medical","slug":"unsupervised-anomaly-detection-in-medical","title":"Unsupervised Anomaly Detection in Medical Images with a Memory-augmented Multi-level Cross-attentional Masked Autoencoder","date":"2022-03-22","arxiv_id":"2203.11725","repositories_listed":1,"syntology":null},{"url":"/paper/high-fidelity-gan-inversion-with-padding","slug":"high-fidelity-gan-inversion-with-padding","title":"High-fidelity GAN Inversion with Padding Space","date":"2022-03-21","arxiv_id":"2203.11105","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-road-layout-parsing-with","slug":"self-supervised-road-layout-parsing-with","title":"Self-Supervised Road Layout Parsing with Graph Auto-Encoding","date":"2022-03-21","arxiv_id":"2203.11000","repositories_listed":1,"syntology":null},{"url":"/paper/a-differentiable-two-stage-alignment-scheme","slug":"a-differentiable-two-stage-alignment-scheme","title":"A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift","date":"2022-03-17","arxiv_id":"2203.09294","repositories_listed":1,"syntology":null},{"url":"/paper/image-quality-assessment-for-magnetic","slug":"image-quality-assessment-for-magnetic","title":"Image Quality Assessment for Magnetic Resonance Imaging","date":"2022-03-15","arxiv_id":"2203.07809","repositories_listed":1,"syntology":null},{"url":"/paper/skm-tea-a-dataset-for-accelerated-mri","slug":"skm-tea-a-dataset-for-accelerated-mri","title":"SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation","date":"2022-03-14","arxiv_id":"2203.06823","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/skm-tea-a-dataset-for-accelerated-mri#ran","syntology_url":"https://syntology.ai/paper/2203.06823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.06823"}},"official":{"repos":["stanfordmimi/skm-tea"],"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/coarse-to-fine-sparse-transformer-for","slug":"coarse-to-fine-sparse-transformer-for","title":"Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction","date":"2022-03-09","arxiv_id":"2203.04845","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":8,"n_ran_checked":9,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"13 ran (of which 8 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/coarse-to-fine-sparse-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2203.04845","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04845"}},"official":{"repos":["caiyuanhao1998/MST"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":8,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-performant-and-reliable-undersampled","slug":"towards-performant-and-reliable-undersampled","title":"Towards performant and reliable undersampled MR reconstruction via diffusion model sampling","date":"2022-03-08","arxiv_id":"2203.04292","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/towards-performant-and-reliable-undersampled#ran","syntology_url":"https://syntology.ai/paper/2203.04292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04292"}},"official":{"repos":["cpeng93/diffuserecon"],"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/unrolled-primal-dual-networks-for-lensless","slug":"unrolled-primal-dual-networks-for-lensless","title":"Unrolled Primal-Dual Networks for Lensless Cameras","date":"2022-03-08","arxiv_id":"2203.04353","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unrolled-primal-dual-networks-for-lensless#ran","syntology_url":"https://syntology.ai/paper/2203.04353","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04353"}},"official":{"repos":["oliland/lensless-primal-dual"],"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"]}}},{"url":"/paper/fluid-registration-between-lung-ct-and","slug":"fluid-registration-between-lung-ct-and","title":"Fluid registration between lung CT and stationary chest tomosynthesis images","date":"2022-03-06","arxiv_id":"2203.04958","repositories_listed":1,"syntology":null},{"url":"/paper/measurement-conditioned-denoising-diffusion","slug":"measurement-conditioned-denoising-diffusion","title":"Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction","date":"2022-03-05","arxiv_id":"2203.03623","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-analysis-operator-learning-by","slug":"convolutional-analysis-operator-learning-by","title":"Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks","date":"2022-03-04","arxiv_id":"2203.02166","repositories_listed":1,"syntology":null},{"url":"/paper/deep-deep-learning-with-bart","slug":"deep-deep-learning-with-bart","title":"Deep, Deep Learning with BART","date":"2022-02-28","arxiv_id":"2202.14005","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-frequency-selective-reconstruction","slug":"real-time-frequency-selective-reconstruction","title":"Real-Time Frequency Selective Reconstruction through Register-Based Argmax Calculation","date":"2022-02-28","arxiv_id":"2202.13926","repositories_listed":1,"syntology":null},{"url":"/paper/multi-view-gradient-consistency-for-svbrdf","slug":"multi-view-gradient-consistency-for-svbrdf","title":"Multi-view Gradient Consistency for SVBRDF Estimation of Complex Scenes under Natural Illumination","date":"2022-02-25","arxiv_id":"2202.13017","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainr-uncertainty-quantification-of-end","slug":"uncertainr-uncertainty-quantification-of-end","title":"UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography","date":"2022-02-22","arxiv_id":"2202.10847","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/uncertainr-uncertainty-quantification-of-end#ran","syntology_url":"https://syntology.ai/paper/2202.10847","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.10847"}},"official":{"repos":["bobby-he/uncertainr"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/snowflake-point-deconvolution-for-point-cloud","slug":"snowflake-point-deconvolution-for-point-cloud","title":"Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer","date":"2022-02-18","arxiv_id":"2202.09367","repositories_listed":1,"syntology":null},{"url":"/paper/a-plug-and-play-approach-to-multiparametric","slug":"a-plug-and-play-approach-to-multiparametric","title":"A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers","date":"2022-02-10","arxiv_id":"2202.05269","repositories_listed":1,"syntology":null},{"url":"/paper/miner-multiscale-implicit-neural","slug":"miner-multiscale-implicit-neural","title":"MINER: Multiscale Implicit Neural Representations","date":"2022-02-07","arxiv_id":"2202.03532","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/miner-multiscale-implicit-neural#ran","syntology_url":"https://syntology.ai/paper/2202.03532","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.03532"}},"official":null}},{"url":"/paper/wave-encoded-model-based-deep-learning-for","slug":"wave-encoded-model-based-deep-learning-for","title":"Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction","date":"2022-02-06","arxiv_id":"2202.02814","repositories_listed":1,"syntology":null},{"url":"/paper/feature-style-encoder-for-style-based-gan","slug":"feature-style-encoder-for-style-based-gan","title":"Feature-Style Encoder for Style-Based GAN Inversion","date":"2022-02-04","arxiv_id":"2202.02183","repositories_listed":1,"syntology":null},{"url":"/paper/image-to-image-mlp-mixer-for-image-1","slug":"image-to-image-mlp-mixer-for-image-1","title":"Image-to-Image MLP-mixer for Image Reconstruction","date":"2022-02-04","arxiv_id":"2202.02018","repositories_listed":1,"syntology":null},{"url":"/paper/a-bayesian-based-deep-unrolling-algorithm-for","slug":"a-bayesian-based-deep-unrolling-algorithm-for","title":"A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems","date":"2022-01-26","arxiv_id":"2201.10910","repositories_listed":1,"syntology":null},{"url":"/paper/event-based-video-reconstruction-via","slug":"event-based-video-reconstruction-via","title":"Event-based Video Reconstruction via Potential-assisted Spiking Neural Network","date":"2022-01-25","arxiv_id":"2201.10943","repositories_listed":1,"syntology":null},{"url":"/paper/surds-self-supervised-attention-guided","slug":"surds-self-supervised-attention-guided","title":"SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature Verification","date":"2022-01-25","arxiv_id":"2201.10138","repositories_listed":1,"syntology":null},{"url":"/paper/sen12ms-cr-ts-a-remote-sensing-data-set-for","slug":"sen12ms-cr-ts-a-remote-sensing-data-set-for","title":"SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal","date":"2022-01-24","arxiv_id":"2201.09613","repositories_listed":1,"syntology":null},{"url":"/paper/can-we-find-neurons-that-cause-unrealistic","slug":"can-we-find-neurons-that-cause-unrealistic","title":"Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?","date":"2022-01-17","arxiv_id":"2201.06346","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-domain-adaptation-for-cross-2","slug":"unsupervised-domain-adaptation-for-cross-2","title":"Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning","date":"2022-01-13","arxiv_id":"2201.04812","repositories_listed":1,"syntology":null},{"url":"/paper/model-based-image-signal-processors-via","slug":"model-based-image-signal-processors-via","title":"Model-Based Image Signal Processors via Learnable Dictionaries","date":"2022-01-10","arxiv_id":"2201.03210","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-aware-auto-encoders-for-self","slug":"semantic-aware-auto-encoders-for-self","title":"Semantic-Aware Auto-Encoders for Self-Supervised Representation Learning","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/calibrated-hyperspectral-image-reconstruction","slug":"calibrated-hyperspectral-image-reconstruction","title":"Modeling Mask Uncertainty in Hyperspectral Image Reconstruction","date":"2021-12-31","arxiv_id":"2112.15362","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":2,"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/calibrated-hyperspectral-image-reconstruction#ran","syntology_url":"https://syntology.ai/paper/2112.15362","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.15362"}},"official":{"repos":["jiamian-wang/mask_uncertainty_spectral_sci"],"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","unlocated"]}}},{"url":"/paper/adjust-a-dictionary-based-joint","slug":"adjust-a-dictionary-based-joint","title":"ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography","date":"2021-12-21","arxiv_id":"2112.11406","repositories_listed":1,"syntology":null},{"url":"/paper/wasserstein-generative-learning-of","slug":"wasserstein-generative-learning-of","title":"Wasserstein Generative Learning of Conditional Distribution","date":"2021-12-19","arxiv_id":"2112.10039","repositories_listed":1,"syntology":null},{"url":"/paper/learned-half-quadratic-splitting-network-for","slug":"learned-half-quadratic-splitting-network-for","title":"Learned Half-Quadratic Splitting Network for MR Image Reconstruction","date":"2021-12-17","arxiv_id":"2112.09760","repositories_listed":1,"syntology":null},{"url":"/paper/towards-end-to-end-image-compression-and","slug":"towards-end-to-end-image-compression-and","title":"Towards End-to-End Image Compression and Analysis with Transformers","date":"2021-12-17","arxiv_id":"2112.09300","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/measuring-complexity-of-learning-schemes","slug":"measuring-complexity-of-learning-schemes","title":"Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation","date":"2021-12-12","arxiv_id":"2112.06209","repositories_listed":1,"syntology":null},{"url":"/paper/specificity-preserving-federated-learning-for","slug":"specificity-preserving-federated-learning-for","title":"Specificity-Preserving Federated Learning for MR Image Reconstruction","date":"2021-12-09","arxiv_id":"2112.05752","repositories_listed":1,"syntology":null}],"record_sha256":"217e73a80e61bb47d46275ed14c6f0a9aee3ade3e8aaee0e1e540dd27e8250ee","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}