{"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":"/method/concatenated-skip-connection/papers/34","list_of":"/method/concatenated-skip-connection","method":"Concatenated Skip Connection","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":34,"pages_in_order":34,"rows_per_page":100,"rows":[3301,3337],"of":3337,"counts":{"archive_papers_tagged":3337,"with_a_code_link":1339,"where_syntology_ran_a_sample":224,"not_listed_spam_title":0,"listed":3337,"listed_where_code_ran":224,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":190,"every_run_a_failure_of_syntologys_instrument":34,"listed_with_a_run_with_no_instrument_failure":190,"listed_every_run_a_failure_of_syntologys_instrument":34,"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":"/method/concatenated-skip-connection","prev":"/method/concatenated-skip-connection/papers/33","next":null,"papers":[{"paper":null,"slug":"skin-lesion-segmentation-u-nets-versus","title":"Skin Lesion Segmentation: U-Nets versus Clustering","date":"2017-09-27","arxiv_id":"1710.01248","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-feasibility-study-for-predicting-optimal","title":"A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning","date":"2017-09-26","arxiv_id":"1709.09233","n_code_links":0,"syntology":null},{"paper":"/paper/a-deep-structured-learning-approach-towards","slug":"a-deep-structured-learning-approach-towards","title":"Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction","date":"2017-09-09","arxiv_id":"1709.02974","n_code_links":1,"syntology":null},{"paper":"/paper/deepunet-a-deep-fully-convolutional-network","slug":"deepunet-a-deep-fully-convolutional-network","title":"DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation","date":"2017-09-01","arxiv_id":"1709.00201","n_code_links":2,"syntology":null},{"paper":"/paper/framing-u-net-via-deep-convolutional","slug":"framing-u-net-via-deep-convolutional","title":"Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT","date":"2017-08-28","arxiv_id":"1708.08333","n_code_links":3,"syntology":null},{"paper":"/paper/learning-efficient-convolutional-networks","slug":"learning-efficient-convolutional-networks","title":"Learning Efficient Convolutional Networks through Network Slimming","date":"2017-08-22","arxiv_id":"1708.06519","n_code_links":12,"syntology":null},{"paper":"/paper/tags2parts-discovering-semantic-regions-from","slug":"tags2parts-discovering-semantic-regions-from","title":"Tags2Parts: Discovering Semantic Regions from Shape Tags","date":"2017-08-22","arxiv_id":"1708.06673","n_code_links":1,"syntology":null},{"paper":"/paper/picanet-learning-pixel-wise-contextual","slug":"picanet-learning-pixel-wise-contextual","title":"PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection","date":"2017-08-21","arxiv_id":"1708.06433","n_code_links":2,"syntology":null},{"paper":"/paper/smash-one-shot-model-architecture-search","slug":"smash-one-shot-model-architecture-search","title":"SMASH: One-Shot Model Architecture Search through HyperNetworks","date":"2017-08-17","arxiv_id":"1708.05344","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-improved-neural-segmentation-method-based","title":"An Improved Neural Segmentation Method Based on U-NET","date":"2017-08-16","arxiv_id":"1708.04747","n_code_links":0,"syntology":null},{"paper":null,"slug":"anisotropic-em-segmentation-by-3d-affinity","title":"Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration","date":"2017-07-27","arxiv_id":"1707.08935","n_code_links":0,"syntology":null},{"paper":"/paper/residual-conv-deconv-grid-network-for","slug":"residual-conv-deconv-grid-network-for","title":"Residual Conv-Deconv Grid Network for Semantic Segmentation","date":"2017-07-25","arxiv_id":"1707.07958","n_code_links":1,"syntology":null},{"paper":"/paper/memory-efficient-implementation-of-densenets","slug":"memory-efficient-implementation-of-densenets","title":"Memory-Efficient Implementation of DenseNets","date":"2017-07-21","arxiv_id":"1707.06990","n_code_links":6,"syntology":null},{"paper":"/paper/efficient-architecture-search-by-network","slug":"efficient-architecture-search-by-network","title":"Efficient Architecture Search by Network Transformation","date":"2017-07-16","arxiv_id":"1707.04873","n_code_links":3,"syntology":null},{"paper":null,"slug":"do-convolutional-networks-need-to-be-deep-for","title":"Do Convolutional Networks need to be Deep for Text Classification ?","date":"2017-07-13","arxiv_id":"1707.04108","n_code_links":0,"syntology":null},{"paper":"/paper/dual-path-networks","slug":"dual-path-networks","title":"Dual Path Networks","date":"2017-07-06","arxiv_id":"1707.01629","n_code_links":18,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"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","official":null}},{"paper":null,"slug":"shadho-massively-scalable-hardware-aware","title":"SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization","date":"2017-07-05","arxiv_id":"1707.01428","n_code_links":0,"syntology":null},{"paper":"/paper/multi-scale-multi-band-densenets-for-audio","slug":"multi-scale-multi-band-densenets-for-audio","title":"Multi-scale Multi-band DenseNets for Audio Source Separation","date":"2017-06-29","arxiv_id":"1706.09588","n_code_links":5,"syntology":null},{"paper":"/paper/style-transfer-for-anime-sketches-with","slug":"style-transfer-for-anime-sketches-with","title":"Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN","date":"2017-06-11","arxiv_id":"1706.03319","n_code_links":2,"syntology":null},{"paper":null,"slug":"deep-learning-for-isotropic-super-resolution","title":"Deep Learning for Isotropic Super-Resolution from Non-Isotropic 3D Electron Microscopy","date":"2017-06-09","arxiv_id":"1706.03142","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-the-reliability-of-out-of","slug":"enhancing-the-reliability-of-out-of","title":"Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks","date":"2017-06-08","arxiv_id":"1706.02690","n_code_links":9,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["facebookresearch/odin"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/segan-adversarial-network-with-multi-scale","slug":"segan-adversarial-network-with-multi-scale","title":"SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation","date":"2017-06-06","arxiv_id":"1706.01805","n_code_links":2,"syntology":null},{"paper":null,"slug":"neural-network-based-automatic-liver-tumor","title":"Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering","date":"2017-06-02","arxiv_id":"1706.00842","n_code_links":0,"syntology":null},{"paper":"/paper/superhuman-accuracy-on-the-snemi3d","slug":"superhuman-accuracy-on-the-snemi3d","title":"Superhuman Accuracy on the SNEMI3D Connectomics Challenge","date":"2017-05-31","arxiv_id":"1706.00120","n_code_links":4,"syntology":null},{"paper":null,"slug":"gridnet-with-automatic-shape-prior","title":"GridNet with automatic shape prior registration for automatic MRI cardiac segmentation","date":"2017-05-24","arxiv_id":"1705.08943","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-diagnostics-applying-convolutional","title":"Deep Diagnostics: Applying Convolutional Neural Networks for Vessels Defects Detection","date":"2017-05-17","arxiv_id":"1705.06264","n_code_links":0,"syntology":null},{"paper":null,"slug":"feature-tracking-cardiac-magnetic-resonance","title":"Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization","date":"2017-04-12","arxiv_id":"1704.03660","n_code_links":0,"syntology":null},{"paper":"/paper/optic-disc-and-cup-segmentation-methods-for","slug":"optic-disc-and-cup-segmentation-methods-for","title":"Optic Disc and Cup Segmentation Methods for Glaucoma Detection with Modification of U-Net Convolutional Neural Network","date":"2017-04-04","arxiv_id":"1704.00979","n_code_links":2,"syntology":null},{"paper":"/paper/snapshot-ensembles-train-1-get-m-for-free","slug":"snapshot-ensembles-train-1-get-m-for-free","title":"Snapshot Ensembles: Train 1, get M for free","date":"2017-04-01","arxiv_id":"1704.00109","n_code_links":11,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["gaohuang/SnapshotEnsemble"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/image-to-image-translation-with-conditional","slug":"image-to-image-translation-with-conditional","title":"Image-to-Image Translation with Conditional Adversarial Networks","date":"2016-11-21","arxiv_id":"1611.07004","n_code_links":192,"syntology":{"ran":74,"of":122,"n_ran_checked":68,"n_instrument":6,"unverified":48,"pointer_only":10,"phrase":"74 ran (of which 0 constructed an object rather than computing a result; 68 with no instrument failure: 4 honoured, 4 violated, 60 with no contract checked; 6 where Syntology's instrument failed) · 48 unverified","official":null}},{"paper":"/paper/quasi-recurrent-neural-networks","slug":"quasi-recurrent-neural-networks","title":"Quasi-Recurrent Neural Networks","date":"2016-11-05","arxiv_id":"1611.01576","n_code_links":7,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":null}},{"paper":"/paper/neural-photo-editing-with-introspective","slug":"neural-photo-editing-with-introspective","title":"Neural Photo Editing with Introspective Adversarial Networks","date":"2016-09-22","arxiv_id":"1609.07093","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ajbrock/Neural-Photo-Editor"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/densely-connected-convolutional-networks","slug":"densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","arxiv_id":"1608.06993","n_code_links":146,"syntology":{"ran":48,"of":71,"n_ran_checked":32,"n_instrument":16,"unverified":23,"pointer_only":8,"phrase":"48 ran (of which 0 constructed an object rather than computing a result; 32 with no instrument failure: 1 honoured, 0 violated, 31 with no contract checked; 16 where Syntology's instrument failed) · 23 unverified","official":{"repos":["liuzhuang13/DenseNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/3d-u-net-learning-dense-volumetric","slug":"3d-u-net-learning-dense-volumetric","title":"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation","date":"2016-06-21","arxiv_id":"1606.06650","n_code_links":27,"syntology":{"ran":59,"of":76,"n_ran_checked":42,"n_instrument":17,"unverified":17,"pointer_only":20,"phrase":"59 ran (of which 18 constructed an object rather than computing a result; 42 with no instrument failure: 4 honoured, 0 violated, 38 with no contract checked; 17 where Syntology's instrument failed) · 17 unverified","official":null}},{"paper":"/paper/deep-generative-image-models-using-a-1","slug":"deep-generative-image-models-using-a-1","title":"Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks","date":"2015-06-18","arxiv_id":"1506.05751","n_code_links":1,"syntology":null},{"paper":"/paper/teaching-machines-to-read-and-comprehend","slug":"teaching-machines-to-read-and-comprehend","title":"Teaching Machines to Read and Comprehend","date":"2015-06-10","arxiv_id":"1506.03340","n_code_links":12,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"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","official":{"repos":["deepmind/rc-data"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/u-net-convolutional-networks-for-biomedical","slug":"u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","arxiv_id":"1505.04597","n_code_links":487,"syntology":{"ran":530,"of":757,"n_ran_checked":383,"n_instrument":147,"unverified":227,"pointer_only":426,"phrase":"530 ran (of which 247 constructed an object rather than computing a result; 383 with no instrument failure: 14 honoured, 6 violated, 363 with no contract checked; 147 where Syntology's instrument failed) · 227 unverified","official":null}}],"record_sha256":"6478eecc24ef16c3c0df8b32c0921c7188ad06a4b939051b53724d249bc1bf51","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}