{"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/residual-block/papers/24","list_of":"/method/residual-block","method":"Residual Block","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":24,"pages_in_order":29,"rows_per_page":100,"rows":[2301,2400],"of":2807,"counts":{"archive_papers_tagged":2807,"with_a_code_link":1322,"where_syntology_ran_a_sample":375,"not_listed_spam_title":0,"listed":2807,"listed_where_code_ran":375,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":316,"every_run_a_failure_of_syntologys_instrument":59,"listed_with_a_run_with_no_instrument_failure":316,"listed_every_run_a_failure_of_syntologys_instrument":59,"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/residual-block","prev":"/method/residual-block/papers/23","next":"/method/residual-block/papers/25","papers":[{"paper":null,"slug":"cutting-down-training-memory-by-re-fowarding-1","title":"Cutting Down Training Memory by Re-fowarding","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dana-scalable-out-of-the-box-distributed-asgd","title":"DANA: Scalable Out-of-the-box Distributed ASGD Without Retuning","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/fast-autoaugment","slug":"fast-autoaugment","title":"Fast AutoAugment","date":"2019-05-01","arxiv_id":"1905.00397","n_code_links":11,"syntology":{"ran":37,"of":40,"n_ran_checked":21,"n_instrument":16,"unverified":3,"pointer_only":7,"phrase":"37 ran (of which 2 constructed an object rather than computing a result; 21 with no instrument failure: 0 honoured, 0 violated, 21 with no contract checked; 16 where Syntology's instrument failed) · 3 unverified","official":{"repos":["kakaobrain/fast-autoaugment"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/object-contour-and-edge-detection-with","slug":"object-contour-and-edge-detection-with","title":"Object Contour and Edge Detection with RefineContourNet","date":"2019-04-30","arxiv_id":"1904.13353","n_code_links":2,"syntology":null},{"paper":"/paper/unsupervised-data-augmentation-1","slug":"unsupervised-data-augmentation-1","title":"Unsupervised Data Augmentation for Consistency Training","date":"2019-04-29","arxiv_id":"1904.12848","n_code_links":20,"syntology":{"ran":30,"of":52,"n_ran_checked":22,"n_instrument":8,"unverified":22,"pointer_only":17,"phrase":"30 ran (of which 3 constructed an object rather than computing a result; 22 with no instrument failure: 0 honoured, 0 violated, 22 with no contract checked; 8 where Syntology's instrument failed) · 22 unverified","official":{"repos":["google-research/uda"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":14,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"automatic-traffic-sign-detection-and","title":"Automatic Traffic Sign Detection and Recognition Using SegU-Net and a Modified Tversky Loss Function With L1-Constraint","date":"2019-04-26","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/190411486","slug":"190411486","title":"Making Convolutional Networks Shift-Invariant Again","date":"2019-04-25","arxiv_id":"1904.11486","n_code_links":7,"syntology":{"ran":2,"of":4,"n_ran_checked":0,"n_instrument":2,"unverified":2,"pointer_only":3,"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) · 2 unverified","official":{"repos":["adobe/antialiased-cnns"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"paper":null,"slug":"face-video-generation-from-a-single-image-and","title":"Face Video Generation from a Single Image and Landmarks","date":"2019-04-25","arxiv_id":"1904.11521","n_code_links":0,"syntology":null},{"paper":"/paper/gcnet-non-local-networks-meet-squeeze","slug":"gcnet-non-local-networks-meet-squeeze","title":"GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond","date":"2019-04-25","arxiv_id":"1904.11492","n_code_links":9,"syntology":null},{"paper":null,"slug":"improved-visible-to-ir-image-transformation","title":"Improved visible to IR image transformation using synthetic data augmentation with cycle-consistent adversarial networks","date":"2019-04-25","arxiv_id":"1904.11620","n_code_links":0,"syntology":null},{"paper":"/paper/reppoints-point-set-representation-for-object","slug":"reppoints-point-set-representation-for-object","title":"RepPoints: Point Set Representation for Object Detection","date":"2019-04-25","arxiv_id":"1904.11490","n_code_links":6,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":3,"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","official":{"repos":["microsoft/RepPoints"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"prediction-of-progression-to-alzheimers","title":"Prediction of Progression to Alzheimer's disease with Deep InfoMax","date":"2019-04-24","arxiv_id":"1904.10931","n_code_links":0,"syntology":null},{"paper":"/paper/lung-nodule-classification-using-deep-local","slug":"lung-nodule-classification-using-deep-local","title":"Lung Nodule Classification using Deep Local-Global Networks","date":"2019-04-23","arxiv_id":"1904.10126","n_code_links":1,"syntology":null},{"paper":"/paper/190409925","slug":"190409925","title":"Attention Augmented Convolutional Networks","date":"2019-04-22","arxiv_id":"1904.09925","n_code_links":14,"syntology":{"ran":3,"of":6,"n_ran_checked":1,"n_instrument":2,"unverified":3,"pointer_only":1,"phrase":"3 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; 2 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":"/paper/an-energy-and-gpu-computation-efficient","slug":"an-energy-and-gpu-computation-efficient","title":"An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection","date":"2019-04-22","arxiv_id":"1904.09730","n_code_links":12,"syntology":null},{"paper":null,"slug":"190409410","title":"LEARNet Dynamic Imaging Network for Micro Expression Recognition","date":"2019-04-20","arxiv_id":"1904.09410","n_code_links":0,"syntology":null},{"paper":"/paper/190409460","slug":"190409460","title":"Data-Driven Neuron Allocation for Scale Aggregation Networks","date":"2019-04-20","arxiv_id":"1904.09460","n_code_links":1,"syntology":null},{"paper":null,"slug":"facial-feature-embedded-cyclegan-for-vis-nir","title":"Facial Feature Embedded CycleGAN for VIS-NIR Translation","date":"2019-04-20","arxiv_id":"1904.09464","n_code_links":0,"syntology":null},{"paper":"/paper/190413216","slug":"190413216","title":"Signal2Image Modules in Deep Neural Networks for EEG Classification","date":"2019-04-18","arxiv_id":"1904.13216","n_code_links":1,"syntology":null},{"paper":"/paper/cascaded-partial-decoder-for-fast-and","slug":"cascaded-partial-decoder-for-fast-and","title":"Cascaded Partial Decoder for Fast and Accurate Salient Object Detection","date":"2019-04-18","arxiv_id":"1904.08739","n_code_links":1,"syntology":null},{"paper":"/paper/nas-fpn-learning-scalable-feature-pyramid","slug":"nas-fpn-learning-scalable-feature-pyramid","title":"NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection","date":"2019-04-16","arxiv_id":"1904.07392","n_code_links":8,"syntology":null},{"paper":null,"slug":"on-the-mathematical-understanding-of-resnet","title":"On the Mathematical Understanding of ResNet with Feynman Path Integral","date":"2019-04-16","arxiv_id":"1904.07568","n_code_links":0,"syntology":null},{"paper":null,"slug":"suction-grasp-region-prediction-using-self","title":"Suction Grasp Region Prediction using Self-supervised Learning for Object Picking in Dense Clutter","date":"2019-04-16","arxiv_id":"1904.07402","n_code_links":0,"syntology":null},{"paper":null,"slug":"characterizing-the-variability-in-face","title":"Characterizing the Variability in Face Recognition Accuracy Relative to Race","date":"2019-04-15","arxiv_id":"1904.07325","n_code_links":0,"syntology":null},{"paper":"/paper/improved-precision-and-recall-metric-for","slug":"improved-precision-and-recall-metric-for","title":"Improved Precision and Recall Metric for Assessing Generative Models","date":"2019-04-15","arxiv_id":"1904.06991","n_code_links":10,"syntology":{"ran":30,"of":42,"n_ran_checked":20,"n_instrument":10,"unverified":12,"pointer_only":25,"phrase":"30 ran (of which 12 constructed an object rather than computing a result; 20 with no instrument failure: 5 honoured, 1 violated, 14 with no contract checked; 10 where Syntology's instrument failed) · 12 unverified","official":{"repos":["kynkaat/improved-precision-and-recall-metric"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/rethinking-classification-and-localization-in","slug":"rethinking-classification-and-localization-in","title":"Rethinking Classification and Localization for Object Detection","date":"2019-04-13","arxiv_id":"1904.06493","n_code_links":2,"syntology":null},{"paper":"/paper/uni-em-an-environment-for-deep-neural-network","slug":"uni-em-an-environment-for-deep-neural-network","title":"UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images","date":"2019-04-12","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/unsupervised-speech-domain-adaptation-based","slug":"unsupervised-speech-domain-adaptation-based","title":"Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech Recognition","date":"2019-04-12","arxiv_id":"1904.06086","n_code_links":1,"syntology":null},{"paper":"/paper/compressing-deep-neural-networks-by-matrix","slug":"compressing-deep-neural-networks-by-matrix","title":"Compressing deep neural networks by matrix product operators","date":"2019-04-11","arxiv_id":"1904.06194","n_code_links":1,"syntology":{"ran":2,"of":5,"n_ran_checked":1,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"2 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; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["zfgao66/deeplearning-mpo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"190408505","title":"Dynamic Gesture Recognition by Using CNNs and Star RGB: a Temporal Information Condensation","date":"2019-04-10","arxiv_id":"1904.08505","n_code_links":0,"syntology":null},{"paper":"/paper/c3ae-exploring-the-limits-of-compact-model","slug":"c3ae-exploring-the-limits-of-compact-model","title":"C3AE: Exploring the Limits of Compact Model for Age Estimation","date":"2019-04-10","arxiv_id":"1904.05059","n_code_links":1,"syntology":null},{"paper":"/paper/drop-an-octave-reducing-spatial-redundancy-in","slug":"drop-an-octave-reducing-spatial-redundancy-in","title":"Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution","date":"2019-04-10","arxiv_id":"1904.05049","n_code_links":28,"syntology":{"ran":24,"of":34,"n_ran_checked":11,"n_instrument":13,"unverified":10,"pointer_only":9,"phrase":"24 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 13 where Syntology's instrument failed) · 10 unverified","official":{"repos":["facebookresearch/OctConv"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"dsnet-an-efficient-cnn-for-road-scene","title":"DSNet: An Efficient CNN for Road Scene Segmentation","date":"2019-04-10","arxiv_id":"1904.05022","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-of-a-dual-convolutional-neural","title":"Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery","date":"2019-04-10","arxiv_id":"1904.05304","n_code_links":0,"syntology":null},{"paper":"/paper/soft-conditional-computation","slug":"soft-conditional-computation","title":"CondConv: Conditionally Parameterized Convolutions for Efficient Inference","date":"2019-04-10","arxiv_id":"1904.04971","n_code_links":9,"syntology":null},{"paper":"/paper/weakly-supervised-learning-of-instance","slug":"weakly-supervised-learning-of-instance","title":"Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations","date":"2019-04-10","arxiv_id":"1904.05044","n_code_links":8,"syntology":{"ran":9,"of":10,"n_ran_checked":8,"n_instrument":1,"unverified":1,"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) · 1 unverified","official":null}},{"paper":"/paper/cyclegan-vc2-improved-cyclegan-based-non","slug":"cyclegan-vc2-improved-cyclegan-based-non","title":"CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion","date":"2019-04-09","arxiv_id":"1904.04631","n_code_links":6,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":null}},{"paper":"/paper/foveabox-beyond-anchor-based-object-detector","slug":"foveabox-beyond-anchor-based-object-detector","title":"FoveaBox: Beyond Anchor-based Object Detector","date":"2019-04-08","arxiv_id":"1904.03797","n_code_links":7,"syntology":null},{"paper":"/paper/scsampler-sampling-salient-clips-from-video","slug":"scsampler-sampling-salient-clips-from-video","title":"SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition","date":"2019-04-08","arxiv_id":"1904.04289","n_code_links":0,"syntology":null},{"paper":"/paper/temporal-convolution-for-real-time-keyword","slug":"temporal-convolution-for-real-time-keyword","title":"Temporal Convolution for Real-time Keyword Spotting on Mobile Devices","date":"2019-04-08","arxiv_id":"1904.03814","n_code_links":3,"syntology":null},{"paper":"/paper/adaptively-connected-neural-networks","slug":"adaptively-connected-neural-networks","title":"Adaptively Connected Neural Networks","date":"2019-04-07","arxiv_id":"1904.03579","n_code_links":1,"syntology":null},{"paper":"/paper/high-level-semantic-feature-detectiona-new","slug":"high-level-semantic-feature-detectiona-new","title":"Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection","date":"2019-04-05","arxiv_id":"1904.02948","n_code_links":2,"syntology":null},{"paper":"/paper/libra-r-cnn-towards-balanced-learning-for","slug":"libra-r-cnn-towards-balanced-learning-for","title":"Libra R-CNN: Towards Balanced Learning for Object Detection","date":"2019-04-04","arxiv_id":"1904.02701","n_code_links":6,"syntology":null},{"paper":"/paper/modified-distribution-alignment-for-domain","slug":"modified-distribution-alignment-for-domain","title":"Modified Distribution Alignment for Domain Adaptation with Pre-trained Inception ResNet","date":"2019-04-04","arxiv_id":"1904.02322","n_code_links":2,"syntology":null},{"paper":"/paper/yolact-real-time-instance-segmentation","slug":"yolact-real-time-instance-segmentation","title":"YOLACT: Real-time Instance Segmentation","date":"2019-04-04","arxiv_id":"1904.02689","n_code_links":48,"syntology":{"ran":18,"of":21,"n_ran_checked":13,"n_instrument":5,"unverified":3,"pointer_only":9,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 2 honoured, 1 violated, 10 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","official":{"repos":["dbolya/yolact"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"hybrid-cosine-based-convolutional-neural","title":"Hybrid Cosine Based Convolutional Neural Networks","date":"2019-04-03","arxiv_id":"1904.01987","n_code_links":0,"syntology":null},{"paper":"/paper/fcos-fully-convolutional-one-stage-object","slug":"fcos-fully-convolutional-one-stage-object","title":"FCOS: Fully Convolutional One-Stage Object Detection","date":"2019-04-02","arxiv_id":"1904.01355","n_code_links":87,"syntology":{"ran":37,"of":40,"n_ran_checked":31,"n_instrument":6,"unverified":3,"pointer_only":19,"phrase":"37 ran (of which 0 constructed an object rather than computing a result; 31 with no instrument failure: 2 honoured, 2 violated, 27 with no contract checked; 6 where Syntology's instrument failed) · 3 unverified","official":{"repos":["tianzhi0549/FCOS"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":"/paper/res2net-a-new-multi-scale-backbone","slug":"res2net-a-new-multi-scale-backbone","title":"Res2Net: A New Multi-scale Backbone Architecture","date":"2019-04-02","arxiv_id":"1904.01169","n_code_links":34,"syntology":{"ran":5,"of":9,"n_ran_checked":3,"n_instrument":2,"unverified":4,"pointer_only":9,"phrase":"5 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; 2 where Syntology's instrument failed) · 4 unverified","official":null}},{"paper":null,"slug":"why-resnet-works-residuals-generalize","title":"Why ResNet Works? Residuals Generalize","date":"2019-04-02","arxiv_id":"1904.01367","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-for-large-scale-traffic-sign","slug":"deep-learning-for-large-scale-traffic-sign","title":"Deep Learning for Large-Scale Traffic-Sign Detection and Recognition","date":"2019-04-01","arxiv_id":"1904.00649","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["skokec/detectron-traffic-signs"],"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/reducing-bert-pre-training-time-from-3-days","slug":"reducing-bert-pre-training-time-from-3-days","title":"Large Batch Optimization for Deep Learning: Training BERT in 76 minutes","date":"2019-04-01","arxiv_id":"1904.00962","n_code_links":32,"syntology":{"ran":6,"of":11,"n_ran_checked":6,"n_instrument":0,"unverified":5,"pointer_only":9,"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) · 5 unverified","official":{"repos":["tensorflow/addons"],"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/esfnet-efficient-network-for-building","slug":"esfnet-efficient-network-for-building","title":"ESFNet: Efficient Network for Building Extraction from High-Resolution Aerial Images","date":"2019-03-29","arxiv_id":"1903.12337","n_code_links":1,"syntology":null},{"paper":"/paper/benchmarking-neural-network-robustness-to-2","slug":"benchmarking-neural-network-robustness-to-2","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","date":"2019-03-28","arxiv_id":"1903.12261","n_code_links":14,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"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","official":{"repos":["hendrycks/robustness"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/feature-intertwiner-for-object-detection-1","slug":"feature-intertwiner-for-object-detection-1","title":"Feature Intertwiner for Object Detection","date":"2019-03-28","arxiv_id":"1903.11851","n_code_links":2,"syntology":null},{"paper":"/paper/shape-robust-text-detection-with-progressive-1","slug":"shape-robust-text-detection-with-progressive-1","title":"Shape Robust Text Detection with Progressive Scale Expansion Network","date":"2019-03-28","arxiv_id":"1903.12473","n_code_links":19,"syntology":null},{"paper":"/paper/tensormask-a-foundation-for-dense-object","slug":"tensormask-a-foundation-for-dense-object","title":"TensorMask: A Foundation for Dense Object Segmentation","date":"2019-03-28","arxiv_id":"1903.12174","n_code_links":2,"syntology":null},{"paper":"/paper/network-slimming-by-slimmable-networks","slug":"network-slimming-by-slimmable-networks","title":"AutoSlim: Towards One-Shot Architecture Search for Channel Numbers","date":"2019-03-27","arxiv_id":"1903.11728","n_code_links":10,"syntology":null},{"paper":"/paper/srm-a-style-based-recalibration-module-for","slug":"srm-a-style-based-recalibration-module-for","title":"SRM : A Style-based Recalibration Module for Convolutional Neural Networks","date":"2019-03-26","arxiv_id":"1903.10829","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":2,"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","official":null}},{"paper":"/paper/metapruning-meta-learning-for-automatic","slug":"metapruning-meta-learning-for-automatic","title":"MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning","date":"2019-03-25","arxiv_id":"1903.10258","n_code_links":2,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["liuzechun/MetaPruning"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/srgan-training-dataset-matters","slug":"srgan-training-dataset-matters","title":"SRGAN: Training Dataset Matters","date":"2019-03-24","arxiv_id":"1903.09922","n_code_links":1,"syntology":null},{"paper":"/paper/auto-reid-searching-for-a-part-aware-convnet","slug":"auto-reid-searching-for-a-part-aware-convnet","title":"Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification","date":"2019-03-23","arxiv_id":"1903.09776","n_code_links":3,"syntology":null},{"paper":"/paper/progressive-dnn-compression-a-key-to-achieve","slug":"progressive-dnn-compression-a-key-to-achieve","title":"Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM","date":"2019-03-23","arxiv_id":"1903.09769","n_code_links":2,"syntology":null},{"paper":null,"slug":"semantic-denoising-autoencoders-for-retinal","title":"Semantic denoising autoencoders for retinal optical coherence tomography","date":"2019-03-23","arxiv_id":"1903.09809","n_code_links":0,"syntology":null},{"paper":null,"slug":"human-activity-recognition-for-edge-devices","title":"Human Activity Recognition for Edge Devices","date":"2019-03-18","arxiv_id":"1903.07563","n_code_links":0,"syntology":null},{"paper":"/paper/training-over-parameterized-deep-resnet-is","slug":"training-over-parameterized-deep-resnet-is","title":"Stabilize Deep ResNet with A Sharp Scaling Factor $τ$","date":"2019-03-17","arxiv_id":"1903.07120","n_code_links":1,"syntology":null},{"paper":null,"slug":"inefficiency-of-k-fac-for-large-batch-size","title":"Inefficiency of K-FAC for Large Batch Size Training","date":"2019-03-14","arxiv_id":"1903.06237","n_code_links":0,"syntology":null},{"paper":null,"slug":"cascaded-projection-end-to-end-network","title":"Cascaded Projection: End-to-End Network Compression and Acceleration","date":"2019-03-12","arxiv_id":"1903.04988","n_code_links":0,"syntology":null},{"paper":"/paper/hetconv-heterogeneous-kernel-based","slug":"hetconv-heterogeneous-kernel-based","title":"HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs","date":"2019-03-11","arxiv_id":"1903.04120","n_code_links":1,"syntology":null},{"paper":"/paper/image-privacy-prediction-using-deep-neural","slug":"image-privacy-prediction-using-deep-neural","title":"Image Privacy Prediction Using Deep Neural Networks","date":"2019-03-08","arxiv_id":"1903.03695","n_code_links":1,"syntology":null},{"paper":"/paper/ce-net-context-encoder-network-for-2d-medical","slug":"ce-net-context-encoder-network-for-2d-medical","title":"CE-Net: Context Encoder Network for 2D Medical Image Segmentation","date":"2019-03-07","arxiv_id":"1903.02740","n_code_links":3,"syntology":null},{"paper":"/paper/high-fidelity-image-generation-with-fewer","slug":"high-fidelity-image-generation-with-fewer","title":"High-Fidelity Image Generation With Fewer Labels","date":"2019-03-06","arxiv_id":"1903.02271","n_code_links":1,"syntology":null},{"paper":null,"slug":"bounded-residual-gradient-networks-breg-net","title":"Bounded Residual Gradient Networks (BReG-Net) for Facial Affect Computing","date":"2019-03-05","arxiv_id":"1903.02110","n_code_links":0,"syntology":null},{"paper":"/paper/feature-selective-anchor-free-module-for","slug":"feature-selective-anchor-free-module-for","title":"Feature Selective Anchor-Free Module for Single-Shot Object Detection","date":"2019-03-02","arxiv_id":"1903.00621","n_code_links":4,"syntology":{"ran":2,"of":8,"n_ran_checked":2,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":null}},{"paper":"/paper/gap-generalizable-approximate-graph","slug":"gap-generalizable-approximate-graph","title":"GAP: Generalizable Approximate Graph Partitioning Framework","date":"2019-03-02","arxiv_id":"1903.00614","n_code_links":1,"syntology":null},{"paper":"/paper/mask-scoring-r-cnn","slug":"mask-scoring-r-cnn","title":"Mask Scoring R-CNN","date":"2019-03-01","arxiv_id":"1903.00241","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":2,"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","official":{"repos":["zjhuang22/maskscoring_rcnn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"tampernn-efficient-tampering-detection-of","title":"TamperNN: Efficient Tampering Detection of Deployed Neural Nets","date":"2019-03-01","arxiv_id":"1903.00317","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-robust-resnet-a-small-step-but-a","title":"Towards Robust ResNet: A Small Step but A Giant Leap","date":"2019-02-28","arxiv_id":"1902.10887","n_code_links":0,"syntology":null},{"paper":"/paper/anode-unconditionally-accurate-memory","slug":"anode-unconditionally-accurate-memory","title":"ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs","date":"2019-02-27","arxiv_id":"1902.10298","n_code_links":5,"syntology":{"ran":4,"of":7,"n_ran_checked":4,"n_instrument":0,"unverified":3,"pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"modulated-binary-cliquenet","title":"Modulated binary cliquenet","date":"2019-02-27","arxiv_id":"1902.10460","n_code_links":0,"syntology":null},{"paper":null,"slug":"harmonic-unpaired-image-to-image-translation","title":"Harmonic Unpaired Image-to-image Translation","date":"2019-02-26","arxiv_id":"1902.09727","n_code_links":0,"syntology":null},{"paper":"/paper/bengali-handwritten-character-classification","slug":"bengali-handwritten-character-classification","title":"Bengali Handwritten Character Classification using Transfer Learning on Deep Convolutional Neural Network","date":"2019-02-25","arxiv_id":"1902.11133","n_code_links":1,"syntology":null},{"paper":null,"slug":"tbnetpulmonary-tuberculosis-diagnosing-system","title":"TBNet:Pulmonary Tuberculosis Diagnosing System using Deep Neural Networks","date":"2019-02-24","arxiv_id":"1902.08897","n_code_links":0,"syntology":null},{"paper":null,"slug":"pointit-a-fast-tracking-framework-based-on-3d","title":"PointIT: A Fast Tracking Framework Based on 3D Instance Segmentation","date":"2019-02-18","arxiv_id":"1902.06379","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-semi-supervised-deep-residual-network-for","title":"A semi-supervised deep residual network for mode detection in Wi-Fi signals","date":"2019-02-17","arxiv_id":"1902.06284","n_code_links":0,"syntology":null},{"paper":"/paper/res-se-net-boosting-performance-of-resnets-by","slug":"res-se-net-boosting-performance-of-resnets-by","title":"RES-SE-NET: Boosting Performance of Resnets by Enhancing Bridge-connections","date":"2019-02-16","arxiv_id":"1902.06066","n_code_links":5,"syntology":null},{"paper":null,"slug":"3d-graph-embedding-learning-with-a-structure","title":"3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation","date":"2019-02-14","arxiv_id":"1902.05247","n_code_links":0,"syntology":null},{"paper":"/paper/multigrain-a-unified-image-embedding-for","slug":"multigrain-a-unified-image-embedding-for","title":"MultiGrain: a unified image embedding for classes and instances","date":"2019-02-14","arxiv_id":"1902.05509","n_code_links":3,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/multigrain"],"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/improved-knowledge-distillation-via-teacher","slug":"improved-knowledge-distillation-via-teacher","title":"Improved Knowledge Distillation via Teacher Assistant","date":"2019-02-09","arxiv_id":"1902.03393","n_code_links":3,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["imirzadeh/Teacher-Assistant-Knowledge-Distillation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/reversible-gans-for-memory-efficient-image-to","slug":"reversible-gans-for-memory-efficient-image-to","title":"Reversible GANs for Memory-efficient Image-to-Image Translation","date":"2019-02-07","arxiv_id":"1902.02729","n_code_links":3,"syntology":null},{"paper":null,"slug":"alphastar-an-evolutionary-computation","title":"AlphaStar: An Evolutionary Computation Perspective","date":"2019-02-05","arxiv_id":"1902.01724","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-single-image-fog-removal-using","title":"End-to-End Single Image Fog Removal using Enhanced Cycle Consistent Adversarial Networks","date":"2019-02-04","arxiv_id":"1902.01374","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparison-of-patch-based-conditional","title":"Comparison of Patch-Based Conditional Generative Adversarial Neural Net Models with Emphasis on Model Robustness for Use in Head and Neck Cases for MR-Only planning","date":"2019-02-01","arxiv_id":"1902.00536","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-hybrid-network-architectures-for","title":"Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge","date":"2019-02-01","arxiv_id":"1902.00460","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-clean-a-gan-perspective","title":"Learning to Clean: A GAN Perspective","date":"2019-01-28","arxiv_id":"1901.11382","n_code_links":0,"syntology":null},{"paper":null,"slug":"tunet-incorporating-segmentation-maps-to","title":"TUNet: Incorporating segmentation maps to improve classification","date":"2019-01-27","arxiv_id":"1901.11379","n_code_links":0,"syntology":null},{"paper":"/paper/progressive-image-deraining-networks-a-better","slug":"progressive-image-deraining-networks-a-better","title":"Progressive Image Deraining Networks: A Better and Simpler Baseline","date":"2019-01-26","arxiv_id":"1901.09221","n_code_links":4,"syntology":{"ran":9,"of":9,"n_ran_checked":9,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["csdwren/PReNet"],"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/equivariant-transformer-networks","slug":"equivariant-transformer-networks","title":"Equivariant Transformer Networks","date":"2019-01-25","arxiv_id":"1901.11399","n_code_links":3,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["stanford-futuredata/equivariant-transformers"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/revisiting-self-supervised-visual","slug":"revisiting-self-supervised-visual","title":"Revisiting Self-Supervised Visual Representation Learning","date":"2019-01-25","arxiv_id":"1901.09005","n_code_links":6,"syntology":{"ran":9,"of":15,"n_ran_checked":9,"n_instrument":0,"unverified":6,"pointer_only":7,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["google/revisiting-self-supervised"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/in-defense-of-the-triplet-loss-for-visual","slug":"in-defense-of-the-triplet-loss-for-visual","title":"Boosting Standard Classification Architectures Through a Ranking Regularizer","date":"2019-01-24","arxiv_id":"1901.08616","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-cyclegans-for-effectively-reducing","title":"Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation","date":"2019-01-24","arxiv_id":"1901.08379","n_code_links":0,"syntology":null}],"record_sha256":"c49cab32c534715dd8c6a492548ee8bf90539a614c4ebc74f03345f370088993","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}