{"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/max-pooling/papers/62","list_of":"/method/max-pooling","method":"Max Pooling","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":62,"pages_in_order":72,"rows_per_page":100,"rows":[6101,6200],"of":7126,"counts":{"archive_papers_tagged":7126,"with_a_code_link":2898,"where_syntology_ran_a_sample":640,"not_listed_spam_title":0,"listed":7126,"listed_where_code_ran":640,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":531,"every_run_a_failure_of_syntologys_instrument":109,"listed_with_a_run_with_no_instrument_failure":531,"listed_every_run_a_failure_of_syntologys_instrument":109,"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/max-pooling","prev":"/method/max-pooling/papers/61","next":"/method/max-pooling/papers/63","papers":[{"paper":null,"slug":"channel-wise-pruning-of-neural-networks-with","title":"Channel-wise pruning of neural networks with tapering resource constraint","date":"2018-12-04","arxiv_id":"1812.07060","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-fuse-things-and-stuff","slug":"learning-to-fuse-things-and-stuff","title":"Learning to Fuse Things and Stuff","date":"2018-12-04","arxiv_id":"1812.01192","n_code_links":0,"syntology":null},{"paper":null,"slug":"brain-tumor-segmentation-using-an-ensemble-of","title":"Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features","date":"2018-12-03","arxiv_id":"1812.01049","n_code_links":0,"syntology":null},{"paper":null,"slug":"identification-and-recognition-of-rice","title":"Identification and Recognition of Rice Diseases and Pests Using Convolutional Neural Networks","date":"2018-12-03","arxiv_id":"1812.01043","n_code_links":0,"syntology":null},{"paper":null,"slug":"mdu-net-multi-scale-densely-connected-u-net","title":"MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation","date":"2018-12-02","arxiv_id":"1812.00352","n_code_links":0,"syntology":null},{"paper":"/paper/can-we-gain-more-from-orthogonality-1","slug":"can-we-gain-more-from-orthogonality-1","title":"Can We Gain More from Orthogonality Regularizations in Training Deep Networks?","date":"2018-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"kalman-normalization-normalizing-internal","title":"Kalman Normalization: Normalizing Internal Representations Across Network Layers","date":"2018-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/pelee-a-real-time-object-detection-system-on-1","slug":"pelee-a-real-time-object-detection-system-on-1","title":"Pelee: A Real-Time Object Detection System on Mobile Devices","date":"2018-12-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/revisiting-multi-task-learning-with-rock-a","slug":"revisiting-multi-task-learning-with-rock-a","title":"Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection","date":"2018-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/symbolic-graph-reasoning-meets-convolutions","slug":"symbolic-graph-reasoning-meets-convolutions","title":"Symbolic Graph Reasoning Meets Convolutions","date":"2018-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/graph-based-global-reasoning-networks","slug":"graph-based-global-reasoning-networks","title":"Graph-Based Global Reasoning Networks","date":"2018-11-30","arxiv_id":"1811.12814","n_code_links":9,"syntology":{"ran":15,"of":15,"n_ran_checked":10,"n_instrument":5,"unverified":0,"pointer_only":7,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/GloRe"],"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":["listed","official"]}}},{"paper":"/paper/making-classification-competitive-for-deep","slug":"making-classification-competitive-for-deep","title":"Classification is a Strong Baseline for Deep Metric Learning","date":"2018-11-30","arxiv_id":"1811.12649","n_code_links":2,"syntology":{"ran":1,"of":5,"n_ran_checked":1,"n_instrument":0,"unverified":4,"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) · 4 unverified","official":{"repos":["azgo14/classification_metric_learning"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"effective-fast-and-memory-efficient","title":"Effective, Fast, and Memory-Efficient Compressed Multi-function Convolutional Neural Networks for More Accurate Medical Image Classification","date":"2018-11-29","arxiv_id":"1811.11996","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-semantic-segmentation-for-visual","title":"Efficient Semantic Segmentation for Visual Bird's-eye View Interpretation","date":"2018-11-29","arxiv_id":"1811.12008","n_code_links":0,"syntology":null},{"paper":"/paper/grid-r-cnn","slug":"grid-r-cnn","title":"Grid R-CNN","date":"2018-11-29","arxiv_id":"1811.12030","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/imagenet-trained-cnns-are-biased-towards","slug":"imagenet-trained-cnns-are-biased-towards","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","date":"2018-11-29","arxiv_id":"1811.12231","n_code_links":7,"syntology":{"ran":3,"of":6,"n_ran_checked":3,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["rgeirhos/Stylized-ImageNet"],"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":"networks-for-nonlinear-diffusion-problems-in","title":"Networks for Nonlinear Diffusion Problems in Imaging","date":"2018-11-29","arxiv_id":"1811.12084","n_code_links":0,"syntology":null},{"paper":"/paper/self-supervised-spatiotemporal-feature","slug":"self-supervised-spatiotemporal-feature","title":"Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction","date":"2018-11-28","arxiv_id":"1811.11387","n_code_links":0,"syntology":null},{"paper":"/paper/strike-with-a-pose-neural-networks-are-easily","slug":"strike-with-a-pose-neural-networks-are-easily","title":"Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects","date":"2018-11-28","arxiv_id":"1811.11553","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-coarse-to-fine-deep-convolutional-neural","title":"A Coarse-to-fine Deep Convolutional Neural Network Framework for Frame Duplication Detection and Localization in Forged Videos","date":"2018-11-27","arxiv_id":"1811.10762","n_code_links":0,"syntology":null},{"paper":"/paper/a-fully-sequential-methodology-for","slug":"a-fully-sequential-methodology-for","title":"Sequentially Aggregated Convolutional Networks","date":"2018-11-27","arxiv_id":"1811.10798","n_code_links":1,"syntology":null},{"paper":"/paper/deformable-convnets-v2-more-deformable-better","slug":"deformable-convnets-v2-more-deformable-better","title":"Deformable ConvNets v2: More Deformable, Better Results","date":"2018-11-27","arxiv_id":"1811.11168","n_code_links":26,"syntology":{"ran":10,"of":13,"n_ran_checked":9,"n_instrument":1,"unverified":3,"pointer_only":4,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":"/paper/dense-xunit-networks","slug":"dense-xunit-networks","title":"Dense xUnit Networks","date":"2018-11-27","arxiv_id":"1811.11051","n_code_links":1,"syntology":null},{"paper":null,"slug":"skin-lesion-segmentation-using-u-net-and-good","title":"Skin lesion segmentation using U-Net and good training strategies","date":"2018-11-27","arxiv_id":"1811.11314","n_code_links":0,"syntology":null},{"paper":"/paper/enresnet-resnet-ensemble-via-the-feynman-kac","slug":"enresnet-resnet-ensemble-via-the-feynman-kac","title":"ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies","date":"2018-11-26","arxiv_id":"1811.10745","n_code_links":5,"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":{"repos":["BaoWangMath/EnResNet"],"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"]}}},{"paper":null,"slug":"stacked-spatio-temporal-graph-convolutional","title":"Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation","date":"2018-11-26","arxiv_id":"1811.10575","n_code_links":0,"syntology":null},{"paper":"/paper/automatic-seismic-salt-interpretation-with","slug":"automatic-seismic-salt-interpretation-with","title":"Automatic Seismic Salt Interpretation with Deep Convolutional Neural Networks","date":"2018-11-24","arxiv_id":"1812.01101","n_code_links":1,"syntology":null},{"paper":null,"slug":"fanet-quality-aware-feature-aggregation","title":"FANet: Quality-Aware Feature Aggregation Network for Robust RGB-T Tracking","date":"2018-11-24","arxiv_id":"1811.09855","n_code_links":0,"syntology":null},{"paper":null,"slug":"forward-stability-of-resnet-and-its-variants","title":"Forward Stability of ResNet and Its Variants","date":"2018-11-24","arxiv_id":"1811.09885","n_code_links":0,"syntology":null},{"paper":"/paper/self-supervised-video-representation-learning","slug":"self-supervised-video-representation-learning","title":"Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles","date":"2018-11-24","arxiv_id":"1811.09795","n_code_links":0,"syntology":null},{"paper":"/paper/181201429","slug":"181201429","title":"Automatic salt deposits segmentation: A deep learning approach","date":"2018-11-21","arxiv_id":"1812.01429","n_code_links":2,"syntology":null},{"paper":"/paper/graph-refinement-based-tree-extraction-using","slug":"graph-refinement-based-tree-extraction-using","title":"Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks","date":"2018-11-21","arxiv_id":"1811.08674","n_code_links":1,"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":["raghavian/graph_refinement"],"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"]}}},{"paper":null,"slug":"integrating-reinforcement-learning-to-self","title":"Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays","date":"2018-11-21","arxiv_id":"1811.08840","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-imagenet-pre-training","slug":"rethinking-imagenet-pre-training","title":"Rethinking ImageNet Pre-training","date":"2018-11-21","arxiv_id":"1811.08883","n_code_links":1,"syntology":null},{"paper":"/paper/retina-u-net-embarrassingly-simple","slug":"retina-u-net-embarrassingly-simple","title":"Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection","date":"2018-11-21","arxiv_id":"1811.08661","n_code_links":6,"syntology":null},{"paper":"/paper/artificial-color-constancy-via-googlenet-with","slug":"artificial-color-constancy-via-googlenet-with","title":"Artificial Color Constancy via GoogLeNet with Angular Loss Function","date":"2018-11-20","arxiv_id":"1811.08456","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-better-features-for-face-detection","title":"Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision","date":"2018-11-20","arxiv_id":"1811.08557","n_code_links":0,"syntology":null},{"paper":null,"slug":"stability-based-filter-pruning-for","title":"Stability Based Filter Pruning for Accelerating Deep CNNs","date":"2018-11-20","arxiv_id":"1811.08321","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-pretrained-densenet-encoder-for-brain-tumor","title":"A Pretrained DenseNet Encoder for Brain Tumor Segmentation","date":"2018-11-19","arxiv_id":"1811.07542","n_code_links":0,"syntology":null},{"paper":null,"slug":"deeper-interpretability-of-deep-networks","title":"Deeper Interpretability of Deep Networks","date":"2018-11-19","arxiv_id":"1811.07807","n_code_links":0,"syntology":null},{"paper":null,"slug":"fotonnet-a-hw-efficient-object-detection","title":"FotonNet: A HW-Efficient Object Detection System Using 3D-Depth Segmentation and 2D-DNN Classifier","date":"2018-11-19","arxiv_id":"1811.07493","n_code_links":0,"syntology":null},{"paper":"/paper/ivd-net-intervertebral-disc-localization-and","slug":"ivd-net-intervertebral-disc-localization-and","title":"IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet","date":"2018-11-19","arxiv_id":"1811.08305","n_code_links":1,"syntology":null},{"paper":null,"slug":"localisation-via-deep-imagination-learn-the","title":"Localisation via Deep Imagination: learn the features not the map","date":"2018-11-19","arxiv_id":"1811.07583","n_code_links":0,"syntology":null},{"paper":null,"slug":"m2u-net-effective-and-efficient-retinal","title":"M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments","date":"2018-11-19","arxiv_id":"1811.07738","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-domain-adaptation-an-adaptive","slug":"unsupervised-domain-adaptation-an-adaptive","title":"Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation","date":"2018-11-19","arxiv_id":"1811.07456","n_code_links":3,"syntology":null},{"paper":"/paper/glstylenet-higher-quality-style-transfer","slug":"glstylenet-higher-quality-style-transfer","title":"GLStyleNet: Higher Quality Style Transfer Combining Global and Local Pyramid Features","date":"2018-11-18","arxiv_id":"1811.07260","n_code_links":1,"syntology":null},{"paper":null,"slug":"multimodal-densenet","title":"Multimodal Densenet","date":"2018-11-18","arxiv_id":"1811.07407","n_code_links":0,"syntology":null},{"paper":"/paper/sequential-image-based-attention-network-for","slug":"sequential-image-based-attention-network-for","title":"Sequential Image-based Attention Network for Inferring Force Estimation without Haptic Sensor","date":"2018-11-17","arxiv_id":"1811.07190","n_code_links":1,"syntology":null},{"paper":null,"slug":"composite-binary-decomposition-networks","title":"Composite Binary Decomposition Networks","date":"2018-11-16","arxiv_id":"1811.06668","n_code_links":0,"syntology":null},{"paper":"/paper/gpipe-efficient-training-of-giant-neural","slug":"gpipe-efficient-training-of-giant-neural","title":"GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism","date":"2018-11-16","arxiv_id":"1811.06965","n_code_links":13,"syntology":{"ran":20,"of":25,"n_ran_checked":19,"n_instrument":1,"unverified":5,"pointer_only":16,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","official":null}},{"paper":"/paper/residual-convolutional-neural-network","slug":"residual-convolutional-neural-network","title":"Residual Convolutional Neural Network Revisited with Active Weighted Mapping","date":"2018-11-16","arxiv_id":"1811.06878","n_code_links":1,"syntology":null},{"paper":null,"slug":"quenn-quantization-engine-for-low-power","title":"QUENN: QUantization Engine for low-power Neural Networks","date":"2018-11-14","arxiv_id":"1811.05896","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-driven-governing-equations-approximation","title":"Data Driven Governing Equations Approximation Using Deep Neural Networks","date":"2018-11-13","arxiv_id":"1811.05537","n_code_links":0,"syntology":null},{"paper":"/paper/gradient-harmonized-single-stage-detector","slug":"gradient-harmonized-single-stage-detector","title":"Gradient Harmonized Single-stage Detector","date":"2018-11-13","arxiv_id":"1811.05181","n_code_links":9,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["libuyu/GHM_Detection"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":"/paper/m2det-a-single-shot-object-detector-based-on","slug":"m2det-a-single-shot-object-detector-based-on","title":"M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network","date":"2018-11-12","arxiv_id":"1811.04533","n_code_links":11,"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: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"reset-learning-recurrent-dynamic-routing-in","title":"ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks","date":"2018-11-11","arxiv_id":"1811.04380","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-brain-structures-segmentation-using","title":"Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net","date":"2018-11-10","arxiv_id":"1811.04312","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-approach-for-building-detection","title":"Deep Learning Approach for Building Detection in Satellite Multispectral Imagery","date":"2018-11-10","arxiv_id":"1811.04247","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-method-of-multimodal-mri-brain-image","title":"The Method of Multimodal MRI Brain Image Segmentation Based on Differential Geometric Features","date":"2018-11-10","arxiv_id":"1811.04281","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-convergence-theory-for-deep-learning-via","title":"A Convergence Theory for Deep Learning via Over-Parameterization","date":"2018-11-09","arxiv_id":"1811.03962","n_code_links":0,"syntology":null},{"paper":"/paper/biologically-plausible-learning-algorithms","slug":"biologically-plausible-learning-algorithms","title":"Biologically-plausible learning algorithms can scale to large datasets","date":"2018-11-08","arxiv_id":"1811.03567","n_code_links":2,"syntology":null},{"paper":null,"slug":"microscopic-nuclei-classification","title":"Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches","date":"2018-11-08","arxiv_id":"1811.03447","n_code_links":0,"syntology":null},{"paper":null,"slug":"colorunet-a-convolutional-classification","title":"ColorUNet: A convolutional classification approach to colorization","date":"2018-11-07","arxiv_id":"1811.03120","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-feature-transfer-between-localization","title":"Deep feature transfer between localization and segmentation tasks","date":"2018-11-06","arxiv_id":"1811.02539","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-unified-framework-of-dnn-weight-pruning-and","title":"A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM","date":"2018-11-05","arxiv_id":"1811.01907","n_code_links":0,"syntology":null},{"paper":"/paper/ra-unet-a-hybrid-deep-attention-aware-network","slug":"ra-unet-a-hybrid-deep-attention-aware-network","title":"RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans","date":"2018-11-04","arxiv_id":"1811.01328","n_code_links":1,"syntology":null},{"paper":"/paper/dunet-a-deformable-network-for-retinal-vessel","slug":"dunet-a-deformable-network-for-retinal-vessel","title":"DUNet: A deformable network for retinal vessel segmentation","date":"2018-11-03","arxiv_id":"1811.01206","n_code_links":0,"syntology":null},{"paper":"/paper/invertible-residual-networks","slug":"invertible-residual-networks","title":"Invertible Residual Networks","date":"2018-11-02","arxiv_id":"1811.00995","n_code_links":5,"syntology":{"ran":9,"of":9,"n_ran_checked":2,"n_instrument":7,"unverified":0,"pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 7 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"ischemic-stroke-lesion-segmentation-in-ct","title":"Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss","date":"2018-11-02","arxiv_id":"1811.01085","n_code_links":0,"syntology":null},{"paper":"/paper/show-attend-and-read-a-simple-and-strong","slug":"show-attend-and-read-a-simple-and-strong","title":"Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition","date":"2018-11-02","arxiv_id":"1811.00751","n_code_links":8,"syntology":{"ran":7,"of":8,"n_ran_checked":7,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"bi-gans-st-for-perceptual-image-super","title":"Bi-GANs-ST for Perceptual Image Super-resolution","date":"2018-11-01","arxiv_id":"1811.00367","n_code_links":0,"syntology":null},{"paper":null,"slug":"dilated-densenets-for-relational-reasoning","title":"Dilated DenseNets for Relational Reasoning","date":"2018-11-01","arxiv_id":"1811.00410","n_code_links":0,"syntology":null},{"paper":"/paper/accelerating-stochastic-training-for-over","slug":"accelerating-stochastic-training-for-over","title":"Accelerating SGD with momentum for over-parameterized learning","date":"2018-10-31","arxiv_id":"1810.13395","n_code_links":1,"syntology":null},{"paper":null,"slug":"performance-assessment-of-the-deep-learning","title":"Performance assessment of the deep learning technologies in grading glaucoma severity","date":"2018-10-31","arxiv_id":"1810.13376","n_code_links":0,"syntology":null},{"paper":null,"slug":"splinenets-continuous-neural-decision-graphs","title":"SplineNets: Continuous Neural Decision Graphs","date":"2018-10-31","arxiv_id":"1810.13118","n_code_links":0,"syntology":null},{"paper":null,"slug":"structure-learning-of-deep-neural-networks","title":"Structure Learning of Deep Neural Networks with Q-Learning","date":"2018-10-31","arxiv_id":"1810.13155","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-effect-of-learning-strategy-versus","title":"The Effect of Learning Strategy versus Inherent Architecture Properties on the Ability of Convolutional Neural Networks to Develop Transformation Invariance","date":"2018-10-31","arxiv_id":"1810.13128","n_code_links":0,"syntology":null},{"paper":"/paper/a-hybrid-frequency-domainimage-domain-deep","slug":"a-hybrid-frequency-domainimage-domain-deep","title":"A Hybrid Frequency-domain/Image-domain Deep Network for Magnetic Resonance Image Reconstruction","date":"2018-10-30","arxiv_id":"1810.12473","n_code_links":1,"syntology":null},{"paper":"/paper/improving-distant-supervision-with-maxpooled","slug":"improving-distant-supervision-with-maxpooled","title":"Combining Distant and Direct Supervision for Neural Relation Extraction","date":"2018-10-30","arxiv_id":"1810.12956","n_code_links":1,"syntology":null},{"paper":"/paper/investigation-of-enhanced-tacotron-text-to","slug":"investigation-of-enhanced-tacotron-text-to","title":"Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention for pitch accent language","date":"2018-10-29","arxiv_id":"1810.11960","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatically-evolving-cnn-architectures","title":"Automatically Evolving CNN Architectures Based on Blocks","date":"2018-10-28","arxiv_id":"1810.11875","n_code_links":0,"syntology":null},{"paper":"/paper/a-miniaturized-semantic-segmentation-method","slug":"a-miniaturized-semantic-segmentation-method","title":"A Miniaturized Semantic Segmentation Method for Remote Sensing Image","date":"2018-10-27","arxiv_id":"1810.11603","n_code_links":1,"syntology":null},{"paper":null,"slug":"building-footprint-generation-using-improved","title":"Building Footprint Generation Using Improved Generative Adversarial Networks","date":"2018-10-26","arxiv_id":"1810.11224","n_code_links":0,"syntology":null},{"paper":"/paper/spectrogram-channels-u-net-a-source","slug":"spectrogram-channels-u-net-a-source","title":"Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each source","date":"2018-10-26","arxiv_id":"1810.11520","n_code_links":1,"syntology":null},{"paper":null,"slug":"mask-propagation-network-for-video-object","title":"Mask Propagation Network for Video Object Segmentation","date":"2018-10-24","arxiv_id":"1810.10289","n_code_links":0,"syntology":null},{"paper":"/paper/spatiotemporal-cnns-for-pornography-detection","slug":"spatiotemporal-cnns-for-pornography-detection","title":"Spatiotemporal CNNs for Pornography Detection in Videos","date":"2018-10-24","arxiv_id":"1810.10519","n_code_links":1,"syntology":null},{"paper":null,"slug":"baseline-detection-in-historical-documents","title":"Baseline Detection in Historical Documents using Convolutional U-Nets","date":"2018-10-22","arxiv_id":"1810.09343","n_code_links":0,"syntology":null},{"paper":"/paper/can-we-gain-more-from-orthogonality","slug":"can-we-gain-more-from-orthogonality","title":"Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?","date":"2018-10-22","arxiv_id":"1810.09102","n_code_links":1,"syntology":null},{"paper":"/paper/single-image-haze-removal-using-a-generative","slug":"single-image-haze-removal-using-a-generative","title":"Single Image Haze Removal using a Generative Adversarial Network","date":"2018-10-22","arxiv_id":"1810.09479","n_code_links":2,"syntology":null},{"paper":null,"slug":"dermatologist-level-dermoscopy-skin-cancer","title":"Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms","date":"2018-10-21","arxiv_id":"1810.10348","n_code_links":0,"syntology":null},{"paper":null,"slug":"left-ventricle-segmentation-via-optical-flow","title":"Left Ventricle Segmentation via Optical-Flow-Net from Short-axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion","date":"2018-10-20","arxiv_id":"1810.08753","n_code_links":0,"syntology":null},{"paper":"/paper/on-extensions-of-clever-a-neural-network","slug":"on-extensions-of-clever-a-neural-network","title":"On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm","date":"2018-10-19","arxiv_id":"1810.08640","n_code_links":1,"syntology":null},{"paper":"/paper/a-novel-focal-tversky-loss-function-with","slug":"a-novel-focal-tversky-loss-function-with","title":"A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation","date":"2018-10-18","arxiv_id":"1810.07842","n_code_links":6,"syntology":null},{"paper":"/paper/deep-learning-methods-for-reynolds-averaged","slug":"deep-learning-methods-for-reynolds-averaged","title":"Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows","date":"2018-10-18","arxiv_id":"1810.08217","n_code_links":2,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["thunil/Deep-Flow-Prediction"],"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/domain-adaptation-for-semantic-segmentation","slug":"domain-adaptation-for-semantic-segmentation","title":"Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training","date":"2018-10-18","arxiv_id":"1810.07911","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"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) · 0 unverified","official":{"repos":["yzou2/CBST"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/mri-reconstruction-via-cascaded-channel-wise","slug":"mri-reconstruction-via-cascaded-channel-wise","title":"MRI Reconstruction via Cascaded Channel-wise Attention Network","date":"2018-10-18","arxiv_id":"1810.08229","n_code_links":1,"syntology":null},{"paper":"/paper/laddernet-multi-path-networks-based-on-u-net","slug":"laddernet-multi-path-networks-based-on-u-net","title":"LadderNet: Multi-path networks based on U-Net for medical image segmentation","date":"2018-10-17","arxiv_id":"1810.07810","n_code_links":3,"syntology":null},{"paper":"/paper/recognizing-partial-biometric-patterns","slug":"recognizing-partial-biometric-patterns","title":"Recognizing Partial Biometric Patterns","date":"2018-10-17","arxiv_id":"1810.07399","n_code_links":1,"syntology":null},{"paper":"/paper/a-comparison-of-1-d-and-2-d-deep","slug":"a-comparison-of-1-d-and-2-d-deep","title":"A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification","date":"2018-10-16","arxiv_id":"1810.07088","n_code_links":1,"syntology":null},{"paper":null,"slug":"bottleneck-supervised-u-net-for-pixel-wise","title":"Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation","date":"2018-10-16","arxiv_id":"1810.10331","n_code_links":0,"syntology":null}],"record_sha256":"58508a3e3cef4384795c78f8d21628784769b25edf21349cb6c64c411dc7ea36","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}