{"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/55","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":55,"pages_in_order":72,"rows_per_page":100,"rows":[5401,5500],"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/54","next":"/method/max-pooling/papers/56","papers":[{"paper":null,"slug":"field-of-view-extension-in-computed","title":"Field of View Extension in Computed Tomography Using Deep Learning Prior","date":"2019-11-04","arxiv_id":"1911.01178","n_code_links":0,"syntology":null},{"paper":"/paper/pgu-net-progressive-growing-of-u-net-for","slug":"pgu-net-progressive-growing-of-u-net-for","title":"PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation","date":"2019-11-04","arxiv_id":"1911.01062","n_code_links":1,"syntology":null},{"paper":"/paper/adversarial-target-invariant-representation","slug":"adversarial-target-invariant-representation","title":"Generalizing to unseen domains via distribution matching","date":"2019-11-03","arxiv_id":"1911.00804","n_code_links":2,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":null}},{"paper":null,"slug":"enhanced-convolutional-neural-tangent-kernels-1","title":"Enhanced Convolutional Neural Tangent Kernels","date":"2019-11-03","arxiv_id":"1911.00809","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-weather-uncertainty-with-deep","title":"Predicting Weather Uncertainty with Deep Convnets","date":"2019-11-02","arxiv_id":"1911.00630","n_code_links":0,"syntology":null},{"paper":null,"slug":"comb-convolution-for-efficient-convolutional","title":"Comb Convolution for Efficient Convolutional Architecture","date":"2019-11-01","arxiv_id":"1911.00387","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-for-space-variant-deconvolution","title":"Deep Learning for space-variant deconvolution in galaxy surveys","date":"2019-11-01","arxiv_id":"1911.00443","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-video-based-apparent-personality","title":"Multimodal Video-based Apparent Personality Recognition Using Long Short-Term Memory and Convolutional Neural Networks","date":"2019-11-01","arxiv_id":"1911.00381","n_code_links":0,"syntology":null},{"paper":null,"slug":"modified-u-net-mu-net-with-incorporation-of","title":"Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images","date":"2019-10-31","arxiv_id":"1911.00140","n_code_links":0,"syntology":null},{"paper":null,"slug":"very-high-resolution-airborne-polsar-image","title":"Very high resolution Airborne PolSAR Image Classification using Convolutional Neural Networks","date":"2019-10-31","arxiv_id":"1910.14578","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-domain-shift-problem-of-medical-image","title":"The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN","date":"2019-10-30","arxiv_id":"1910.13681","n_code_links":0,"syntology":null},{"paper":null,"slug":"leanconvnets-low-cost-yet-effective","title":"LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks","date":"2019-10-29","arxiv_id":"1910.13157","n_code_links":0,"syntology":null},{"paper":null,"slug":"region-based-convolution-neural-network","title":"Region-based Convolution Neural Network Approach for Accurate Segmentation of Pelvic Radiograph","date":"2019-10-29","arxiv_id":"1910.13231","n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-grained-object-detection-over-scientific","title":"Fine-Grained Object Detection over Scientific Document Images with Region Embeddings","date":"2019-10-28","arxiv_id":"1910.12462","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-sequence-cardiac-mr-segmentation-with","title":"Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network","date":"2019-10-28","arxiv_id":"1910.12514","n_code_links":0,"syntology":null},{"paper":"/paper/skip-clip-self-supervised-spatiotemporal","slug":"skip-clip-self-supervised-spatiotemporal","title":"Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking","date":"2019-10-28","arxiv_id":"1910.12770","n_code_links":0,"syntology":null},{"paper":"/paper/an-adaptive-and-momental-bound-method-for","slug":"an-adaptive-and-momental-bound-method-for","title":"An Adaptive and Momental Bound Method for Stochastic Learning","date":"2019-10-27","arxiv_id":"1910.12249","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: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["lancopku/AdaMod"],"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":null,"slug":"segmenting-ships-in-satellite-imagery-with","title":"Segmenting Ships in Satellite Imagery With Squeeze and Excitation U-Net","date":"2019-10-27","arxiv_id":"1910.12206","n_code_links":0,"syntology":null},{"paper":null,"slug":"solving-optimization-problems-through-fully","title":"Solving Optimization Problems through Fully Convolutional Networks: an Application to the Travelling Salesman Problem","date":"2019-10-27","arxiv_id":"1910.12243","n_code_links":0,"syntology":null},{"paper":"/paper/consistency-regularization-for-generative-1","slug":"consistency-regularization-for-generative-1","title":"Consistency Regularization for Generative Adversarial Networks","date":"2019-10-26","arxiv_id":"1910.12027","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-and-control-algorithms-of","title":"Deep Learning and Control Algorithms of Direct Perception for Autonomous Driving","date":"2019-10-26","arxiv_id":"1910.12031","n_code_links":0,"syntology":null},{"paper":null,"slug":"emotion-recognition-with-4kresolution","title":"Emotion recognition with 4kresolution database","date":"2019-10-24","arxiv_id":"1910.11276","n_code_links":0,"syntology":null},{"paper":"/paper/u-time-a-fully-convolutional-network-for-time","slug":"u-time-a-fully-convolutional-network-for-time","title":"U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging","date":"2019-10-24","arxiv_id":"1910.11162","n_code_links":5,"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":null,"slug":"unified-multi-scale-feature-abstraction-for","title":"Unified Multi-scale Feature Abstraction for Medical Image Segmentation","date":"2019-10-24","arxiv_id":"1910.11456","n_code_links":0,"syntology":null},{"paper":null,"slug":"identification-of-primary-angle-closure-on-as","title":"Identification of primary angle-closure on AS-OCT images with Convolutional Neural Networks","date":"2019-10-23","arxiv_id":"1910.10414","n_code_links":0,"syntology":null},{"paper":"/paper/neural-ordinary-differential-equations-for","slug":"neural-ordinary-differential-equations-for","title":"Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands","date":"2019-10-23","arxiv_id":"1910.10470","n_code_links":2,"syntology":null},{"paper":null,"slug":"semantic-segmentation-of-skin-lesions-using-a","title":"Semantic Segmentation of Skin Lesions using a Small Data Set","date":"2019-10-23","arxiv_id":"1910.10534","n_code_links":0,"syntology":null},{"paper":"/paper/4-connected-shift-residual-networks","slug":"4-connected-shift-residual-networks","title":"4-Connected Shift Residual Networks","date":"2019-10-22","arxiv_id":"1910.09931","n_code_links":1,"syntology":null},{"paper":null,"slug":"establishing-an-evaluation-metric-to-quantify","title":"Establishing an Evaluation Metric to Quantify Climate Change Image Realism","date":"2019-10-22","arxiv_id":"1910.10143","n_code_links":0,"syntology":null},{"paper":"/paper/self-correction-for-human-parsing","slug":"self-correction-for-human-parsing","title":"Self-Correction for Human Parsing","date":"2019-10-22","arxiv_id":"1910.09777","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: 1 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["PeikeLi/Self-Correction-Human-Parsing"],"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":["listed","official"]}}},{"paper":null,"slug":"directed-weighting-group-lasso-for-eltwise","title":"Directed-Weighting Group Lasso for Eltwise Blocked CNN Pruning","date":"2019-10-21","arxiv_id":"1910.09318","n_code_links":0,"syntology":null},{"paper":"/paper/improving-vehicle-re-identification-using-cnn","slug":"improving-vehicle-re-identification-using-cnn","title":"Improving Vehicle Re-Identification using CNN Latent Spaces: Metrics Comparison and Track-to-track Extension","date":"2019-10-21","arxiv_id":"1910.09458","n_code_links":1,"syntology":null},{"paper":null,"slug":"kuronet-pre-modern-japanese-kuzushiji","title":"KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning","date":"2019-10-21","arxiv_id":"1910.09433","n_code_links":0,"syntology":null},{"paper":"/paper/miscnn-a-framework-for-medical-image","slug":"miscnn-a-framework-for-medical-image","title":"MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning","date":"2019-10-21","arxiv_id":"1910.09308","n_code_links":1,"syntology":null},{"paper":"/paper/deep-speech-inpainting-of-time-frequency","slug":"deep-speech-inpainting-of-time-frequency","title":"Deep speech inpainting of time-frequency masks","date":"2019-10-20","arxiv_id":"1910.09058","n_code_links":2,"syntology":null},{"paper":"/paper/musical-instrument-playing-technique","slug":"musical-instrument-playing-technique","title":"Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example","date":"2019-10-20","arxiv_id":"1910.09021","n_code_links":1,"syntology":null},{"paper":null,"slug":"pid-a-new-benchmark-dataset-to-classify-and","title":"Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses","date":"2019-10-20","arxiv_id":"1910.11123","n_code_links":0,"syntology":null},{"paper":null,"slug":"mixmodule-mixed-cnn-kernel-module-for-medical","title":"MixModule: Mixed CNN Kernel Module for Medical Image Segmentation","date":"2019-10-19","arxiv_id":"1910.08728","n_code_links":0,"syntology":null},{"paper":"/paper/spatial-aware-online-adversarial","slug":"spatial-aware-online-adversarial","title":"SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking","date":"2019-10-19","arxiv_id":"1910.08681","n_code_links":1,"syntology":null},{"paper":null,"slug":"tracking-assisted-segmentation-of-biological","title":"Tracking-Assisted Segmentation of Biological Cells","date":"2019-10-19","arxiv_id":"1910.08735","n_code_links":0,"syntology":null},{"paper":"/paper/bobby2-buffer-based-robust-high-speed-object","slug":"bobby2-buffer-based-robust-high-speed-object","title":"BOBBY2: Buffer Based Robust High-Speed Object Tracking","date":"2019-10-18","arxiv_id":"1910.08263","n_code_links":1,"syntology":null},{"paper":"/paper/evading-real-time-person-detectors-by","slug":"evading-real-time-person-detectors-by","title":"Adversarial T-shirt! Evading Person Detectors in A Physical World","date":"2019-10-18","arxiv_id":"1910.11099","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":null}},{"paper":"/paper/intracranial-hemorrhage-segmentation-using","slug":"intracranial-hemorrhage-segmentation-using","title":"Intracranial Hemorrhage Segmentation Using Deep Convolutional Model","date":"2019-10-18","arxiv_id":"1910.08643","n_code_links":1,"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":["Murtadha44/-Intracranial-Hemorrhage-Segmentation-Using-Deep-Convolutional-Model-U-Net-"],"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":"conservation-ai-live-stream-analysis-for-the","title":"Conservation AI: Live Stream Analysis for the Detection of Endangered Species Using Convolutional Neural Networks and Drone Technology","date":"2019-10-16","arxiv_id":"1910.07360","n_code_links":0,"syntology":null},{"paper":"/paper/injecting-hierarchy-with-u-net-transformers","slug":"injecting-hierarchy-with-u-net-transformers","title":"Injecting Hierarchy with U-Net Transformers","date":"2019-10-16","arxiv_id":"1910.10488","n_code_links":2,"syntology":null},{"paper":null,"slug":"offline-handwritten-mathematical-symbol","title":"Offline handwritten mathematical symbol recognition utilising deep learning","date":"2019-10-16","arxiv_id":"1910.07395","n_code_links":0,"syntology":null},{"paper":null,"slug":"analyzing-large-receptive-field-convolutional","title":"Analyzing Large Receptive Field Convolutional Networks for Distant Speech Recognition","date":"2019-10-15","arxiv_id":"1910.07047","n_code_links":0,"syntology":null},{"paper":"/paper/learning-sparsity-and-quantization-jointly","slug":"learning-sparsity-and-quantization-jointly","title":"Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach","date":"2019-10-14","arxiv_id":"1910.05897","n_code_links":1,"syntology":null},{"paper":"/paper/robust-compressive-sensing-mri-reconstruction","slug":"robust-compressive-sensing-mri-reconstruction","title":"Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks","date":"2019-10-14","arxiv_id":"1910.06067","n_code_links":1,"syntology":null},{"paper":"/paper/vertebrae-detection-and-localization-in-ct","slug":"vertebrae-detection-and-localization-in-ct","title":"Vertebrae Detection and Localization in CT with Two-Stage CNNs and Dense Annotations","date":"2019-10-14","arxiv_id":"1910.05911","n_code_links":1,"syntology":null},{"paper":null,"slug":"radiomic-feature-stability-analysis-based-on","title":"Radiomic Feature Stability Analysis based on Probabilistic Segmentations","date":"2019-10-13","arxiv_id":"1910.05693","n_code_links":0,"syntology":null},{"paper":null,"slug":"facial-emotion-recognition-using","title":"Facial Emotion Recognition using Convolutional Neural Networks","date":"2019-10-12","arxiv_id":"1910.05602","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-are-attributes-expressed-in-face-dcnns","title":"How are attributes expressed in face DCNNs?","date":"2019-10-12","arxiv_id":"1910.05657","n_code_links":0,"syntology":null},{"paper":"/paper/aff-wild-database-and-affwildnet","slug":"aff-wild-database-and-affwildnet","title":"Aff-Wild Database and AffWildNet","date":"2019-10-11","arxiv_id":"1910.05318","n_code_links":1,"syntology":null},{"paper":null,"slug":"illegible-text-to-readable-text-an-image-to","title":"Illegible Text to Readable Text: An Image-to-Image Transformation using Conditional Sliced Wasserstein Adversarial Networks","date":"2019-10-11","arxiv_id":"1910.05425","n_code_links":0,"syntology":null},{"paper":null,"slug":"shape-constrained-network-for-eye","title":"Shape Constrained Network for Eye Segmentation in the Wild","date":"2019-10-11","arxiv_id":"1910.05283","n_code_links":0,"syntology":null},{"paper":"/paper/3d-manhattan-room-layout-reconstruction-from","slug":"3d-manhattan-room-layout-reconstruction-from","title":"Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods","date":"2019-10-09","arxiv_id":"1910.04099","n_code_links":3,"syntology":null},{"paper":"/paper/on-the-adequacy-of-untuned-warmup-for","slug":"on-the-adequacy-of-untuned-warmup-for","title":"On the adequacy of untuned warmup for adaptive optimization","date":"2019-10-09","arxiv_id":"1910.04209","n_code_links":1,"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":null}},{"paper":"/paper/pipemare-asynchronous-pipeline-parallel-dnn","slug":"pipemare-asynchronous-pipeline-parallel-dnn","title":"PipeMare: Asynchronous Pipeline Parallel DNN Training","date":"2019-10-09","arxiv_id":"1910.05124","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-multiphase-level-set-for-scene-parsing","title":"Deep Multiphase Level Set for Scene Parsing","date":"2019-10-08","arxiv_id":"1910.03166","n_code_links":0,"syntology":null},{"paper":"/paper/deep-network-classification-by-scattering-and","slug":"deep-network-classification-by-scattering-and","title":"Deep Network Classification by Scattering and Homotopy Dictionary Learning","date":"2019-10-08","arxiv_id":"1910.03561","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":3,"n_instrument":1,"unverified":2,"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) · 2 unverified","official":{"repos":["j-zarka/SparseScatNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/eca-net-efficient-channel-attention-for-deep","slug":"eca-net-efficient-channel-attention-for-deep","title":"ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks","date":"2019-10-08","arxiv_id":"1910.03151","n_code_links":13,"syntology":{"ran":3,"of":8,"n_ran_checked":2,"n_instrument":1,"unverified":5,"pointer_only":1,"phrase":"3 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; 1 where Syntology's instrument failed) · 5 unverified","official":{"repos":["BangguWu/ECANet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/torchbeast-a-pytorch-platform-for-distributed","slug":"torchbeast-a-pytorch-platform-for-distributed","title":"TorchBeast: A PyTorch Platform for Distributed RL","date":"2019-10-08","arxiv_id":"1910.03552","n_code_links":3,"syntology":{"ran":6,"of":9,"n_ran_checked":2,"n_instrument":4,"unverified":3,"pointer_only":3,"phrase":"6 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; 4 where Syntology's instrument failed) · 3 unverified","official":{"repos":["heiner/scalable_agent"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/deformable-kernels-adapting-effective","slug":"deformable-kernels-adapting-effective","title":"Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation","date":"2019-10-07","arxiv_id":"1910.02940","n_code_links":2,"syntology":null},{"paper":null,"slug":"sndcnn-self-normalizing-deep-cnns-with-scaled","title":"SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognition","date":"2019-10-04","arxiv_id":"1910.01992","n_code_links":0,"syntology":null},{"paper":null,"slug":"vulnerability-of-face-recognition-to-deep","title":"Vulnerability of Face Recognition to Deep Morphing","date":"2019-10-03","arxiv_id":"1910.01933","n_code_links":0,"syntology":null},{"paper":"/paper/yolo-nano-a-highly-compact-you-only-look-once","slug":"yolo-nano-a-highly-compact-you-only-look-once","title":"YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection","date":"2019-10-03","arxiv_id":"1910.01271","n_code_links":4,"syntology":null},{"paper":null,"slug":"a-pre-defined-sparse-kernel-based","title":"A Pre-defined Sparse Kernel Based Convolution for Deep CNNs","date":"2019-10-02","arxiv_id":"1910.00724","n_code_links":0,"syntology":null},{"paper":"/paper/w-net-a-cnn-based-architecture-for-white","slug":"w-net-a-cnn-based-architecture-for-white","title":"W-Net: A CNN-based Architecture for White Blood Cells Image Classification","date":"2019-10-02","arxiv_id":"1910.01091","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-bayesian-optimization-framework-for-neural","title":"A Bayesian Optimization Framework for Neural Network Compression","date":"2019-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/df2net-a-dense-fine-finer-network-for","slug":"df2net-a-dense-fine-finer-network-for","title":"DF2Net: A Dense-Fine-Finer Network for Detailed 3D Face Reconstruction","date":"2019-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"dsconv-efficient-convolution-operator-1","title":"DSConv: Efficient Convolution Operator","date":"2019-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"improvement-of-multiparametric-mr-image","title":"Improvement of Multiparametric MR Image Segmentation by Augmenting the Data with Generative Adversarial Networks for Glioma Patients","date":"2019-10-01","arxiv_id":"1910.00696","n_code_links":0,"syntology":null},{"paper":"/paper/learning-rich-features-at-high-speed-for","slug":"learning-rich-features-at-high-speed-for","title":"Learning Rich Features at High-Speed for Single-Shot Object Detection","date":"2019-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/localization-of-deep-inpainting-using-high","slug":"localization-of-deep-inpainting-using-high","title":"Localization of Deep Inpainting Using High-Pass Fully Convolutional Network","date":"2019-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"p-mvsnet-learning-patch-wise-matching","title":"P-MVSNet: Learning Patch-Wise Matching Confidence Aggregation for Multi-View Stereo","date":"2019-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/progressive-reconstruction-of-visual","slug":"progressive-reconstruction-of-visual","title":"Progressive Reconstruction of Visual Structure for Image Inpainting","date":"2019-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/ritnet-real-time-semantic-segmentation-of-the","slug":"ritnet-real-time-semantic-segmentation-of-the","title":"RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking","date":"2019-10-01","arxiv_id":"1910.00694","n_code_links":2,"syntology":null},{"paper":"/paper/small-steps-and-giant-leaps-minimal-newton-2","slug":"small-steps-and-giant-leaps-minimal-newton-2","title":"Small Steps and Giant Leaps: Minimal Newton Solvers for Deep Learning","date":"2019-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/stacked-cross-refinement-network-for-edge","slug":"stacked-cross-refinement-network-for-edge","title":"Stacked Cross Refinement Network for Edge-Aware Salient Object Detection","date":"2019-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/the-visual-task-adaptation-benchmark","slug":"the-visual-task-adaptation-benchmark","title":"A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark","date":"2019-10-01","arxiv_id":"1910.04867","n_code_links":2,"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":["google-research/task_adaptation"],"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":"thundernet-towards-real-time-generic-object-1","title":"ThunderNet: Towards Real-Time Generic Object Detection on Mobile Devices","date":"2019-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/edgecnn-convolutional-neural-network","slug":"edgecnn-convolutional-neural-network","title":"EdgeCNN: Convolutional Neural Network Classification Model with small inputs for Edge Computing","date":"2019-09-30","arxiv_id":"1909.13522","n_code_links":1,"syntology":null},{"paper":"/paper/randaugment-practical-data-augmentation-with","slug":"randaugment-practical-data-augmentation-with","title":"RandAugment: Practical automated data augmentation with a reduced search space","date":"2019-09-30","arxiv_id":"1909.13719","n_code_links":19,"syntology":{"ran":58,"of":65,"n_ran_checked":7,"n_instrument":51,"unverified":7,"pointer_only":17,"phrase":"58 ran (of which 1 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 51 where Syntology's instrument failed) · 7 unverified","official":null}},{"paper":null,"slug":"spatio-temporal-fast-3d-convolutions-for","title":"Spatio-Temporal FAST 3D Convolutions for Human Action Recognition","date":"2019-09-30","arxiv_id":"1909.13474","n_code_links":0,"syntology":null},{"paper":null,"slug":"fusion-of-convolutional-neural-network-and","title":"Fusion of Convolutional Neural Network and Statistical Features for Texture classification","date":"2019-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/pixel-wise-polsar-image-classification-via-a","slug":"pixel-wise-polsar-image-classification-via-a","title":"Pixel-Wise PolSAR Image Classification via a Novel Complex-Valued Deep Fully Convolutional Network","date":"2019-09-29","arxiv_id":"1909.13299","n_code_links":1,"syntology":null},{"paper":"/paper/polarmask-single-shot-instance-segmentation","slug":"polarmask-single-shot-instance-segmentation","title":"PolarMask: Single Shot Instance Segmentation with Polar Representation","date":"2019-09-29","arxiv_id":"1909.13226","n_code_links":2,"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":{"repos":["xieenze/PolarMask"],"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"]}}},{"paper":"/paper/unsharp-masking-layer-injecting-prior","slug":"unsharp-masking-layer-injecting-prior","title":"Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification","date":"2019-09-29","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"vision-based-autonomous-vehicle-control-using","title":"Vision-Based Autonomous Vehicle Control using the Two-Point Visual Driver Control Model","date":"2019-09-29","arxiv_id":"1910.04862","n_code_links":0,"syntology":null},{"paper":null,"slug":"gdp-generalized-device-placement-for-dataflow","title":"GDP: Generalized Device Placement for Dataflow Graphs","date":"2019-09-28","arxiv_id":"1910.01578","n_code_links":0,"syntology":null},{"paper":null,"slug":"point-attention-network-for-semantic","title":"Point Attention Network for Semantic Segmentation of 3D Point Clouds","date":"2019-09-27","arxiv_id":"1909.12663","n_code_links":0,"syntology":null},{"paper":"/paper/balanced-binary-neural-networks-with-gated","slug":"balanced-binary-neural-networks-with-gated","title":"Balanced Binary Neural Networks with Gated Residual","date":"2019-09-26","arxiv_id":"1909.12117","n_code_links":1,"syntology":null},{"paper":null,"slug":"breast-cancer-diagnosis-with-transfer","title":"Breast Cancer Diagnosis with Transfer Learning and Global Pooling","date":"2019-09-26","arxiv_id":"1909.11839","n_code_links":0,"syntology":null},{"paper":null,"slug":"classification-of-histopathological-biopsy","title":"Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks","date":"2019-09-26","arxiv_id":"1909.11870","n_code_links":0,"syntology":null},{"paper":null,"slug":"accurate-and-compact-convolutional-neural","title":"Accurate and Compact Convolutional Neural Networks with Trained Binarization","date":"2019-09-25","arxiv_id":"1909.11366","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-identification-of-neural-cells-in","title":"Automated identification of neural cells in the multi-photon images using deep-neural networks","date":"2019-09-25","arxiv_id":"1909.11269","n_code_links":0,"syntology":null},{"paper":null,"slug":"pydens-a-python-framework-for-solving","title":"PyDEns: a Python Framework for Solving Differential Equations with Neural Networks","date":"2019-09-25","arxiv_id":"1909.11544","n_code_links":0,"syntology":null},{"paper":null,"slug":"residual-networks-behave-like-boosting","title":"Residual Networks Behave Like Boosting Algorithms","date":"2019-09-25","arxiv_id":"1909.11790","n_code_links":0,"syntology":null},{"paper":null,"slug":"speech-recognition-with-augmented-synthesized","title":"Speech Recognition with Augmented Synthesized Speech","date":"2019-09-25","arxiv_id":"1909.11699","n_code_links":0,"syntology":null}],"record_sha256":"b9a424b20d09cf6fec2dbcc2348526905ba948bf7345d993bbac19021166383b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}