{"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/convolution/papers/156","list_of":"/method/convolution","method":"Convolution","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":156,"pages_in_order":196,"rows_per_page":100,"rows":[15501,15600],"of":19586,"counts":{"archive_papers_tagged":19586,"with_a_code_link":8064,"where_syntology_ran_a_sample":1837,"not_listed_spam_title":0,"listed":19586,"listed_where_code_ran":1837,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1557,"every_run_a_failure_of_syntologys_instrument":280,"listed_with_a_run_with_no_instrument_failure":1557,"listed_every_run_a_failure_of_syntologys_instrument":280,"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/convolution","prev":"/method/convolution/papers/155","next":"/method/convolution/papers/157","papers":[{"paper":"/paper/gated-convolutional-networks-with-hybrid","slug":"gated-convolutional-networks-with-hybrid","title":"Gated Convolutional Networks with Hybrid Connectivity for Image Classification","date":"2019-08-26","arxiv_id":"1908.09699","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":0,"n_instrument":4,"unverified":1,"pointer_only":5,"phrase":"4 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; 4 where Syntology's instrument failed) · 1 unverified","official":{"repos":["winycg/HCGNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"high-performance-visual-object-tracking-with","title":"High Performance Visual Object Tracking with Unified Convolutional Networks","date":"2019-08-26","arxiv_id":"1908.09445","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-disentangled-representations-via","title":"Learning Disentangled Representations via Independent Subspaces","date":"2019-08-26","arxiv_id":"1908.08989","n_code_links":0,"syntology":null},{"paper":null,"slug":"mocycle-gan-unpaired-video-to-video","title":"Mocycle-GAN: Unpaired Video-to-Video Translation","date":"2019-08-26","arxiv_id":"1908.09514","n_code_links":0,"syntology":null},{"paper":null,"slug":"see-more-than-once-kernel-sharing-atrous","title":"See More Than Once -- Kernel-Sharing Atrous Convolution for Semantic Segmentation","date":"2019-08-26","arxiv_id":"1908.09443","n_code_links":0,"syntology":null},{"paper":null,"slug":"slidergan-synthesizing-expressive-face-images","title":"SliderGAN: Synthesizing Expressive Face Images by Sliding 3D Blendshape Parameters","date":"2019-08-26","arxiv_id":"1908.09638","n_code_links":0,"syntology":null},{"paper":null,"slug":"stochastic-filter-groups-for-multi-task-cnns","title":"Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels","date":"2019-08-26","arxiv_id":"1908.09597","n_code_links":0,"syntology":null},{"paper":null,"slug":"depth-agmnet-an-atrous-granular-multiscale","title":"Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task","date":"2019-08-25","arxiv_id":"1908.09346","n_code_links":0,"syntology":null},{"paper":"/paper/recon-glgan-a-global-local-context-based","slug":"recon-glgan-a-global-local-context-based","title":"Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction","date":"2019-08-25","arxiv_id":"1908.09262","n_code_links":1,"syntology":null},{"paper":null,"slug":"blended-convolution-and-synthesis-for","title":"Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes","date":"2019-08-24","arxiv_id":"1908.10209","n_code_links":0,"syntology":null},{"paper":null,"slug":"plexus-convolutional-neural-network-plexusnet","title":"Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis","date":"2019-08-24","arxiv_id":"1908.09067","n_code_links":0,"syntology":null},{"paper":null,"slug":"drfn-deep-recurrent-fusion-network-for-single","title":"DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors","date":"2019-08-23","arxiv_id":"1908.08837","n_code_links":0,"syntology":null},{"paper":null,"slug":"generating-high-resolution-fashion-model","title":"Generating High-Resolution Fashion Model Images Wearing Custom Outfits","date":"2019-08-23","arxiv_id":"1908.08847","n_code_links":0,"syntology":null},{"paper":null,"slug":"image-based-cellular-contractile-force","title":"Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet","date":"2019-08-23","arxiv_id":"1908.08631","n_code_links":0,"syntology":null},{"paper":"/paper/mish-a-self-regularized-non-monotonic-neural","slug":"mish-a-self-regularized-non-monotonic-neural","title":"Mish: A Self Regularized Non-Monotonic Activation Function","date":"2019-08-23","arxiv_id":"1908.08681","n_code_links":9,"syntology":{"ran":9,"of":12,"n_ran_checked":8,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["digantamisra98/Mish"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/no-fear-of-the-dark-image-retrieval-under","slug":"no-fear-of-the-dark-image-retrieval-under","title":"No Fear of the Dark: Image Retrieval under Varying Illumination Conditions","date":"2019-08-23","arxiv_id":"1908.08999","n_code_links":1,"syntology":{"ran":1,"of":7,"n_ran_checked":1,"n_instrument":0,"unverified":6,"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) · 6 unverified","official":{"repos":["jenicek/mdir"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"predicting-knee-osteoarthritis-severity","title":"Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images","date":"2019-08-23","arxiv_id":"1908.08873","n_code_links":0,"syntology":null},{"paper":"/paper/shadow-removal-via-shadow-image-decomposition","slug":"shadow-removal-via-shadow-image-decomposition","title":"Shadow Removal via Shadow Image Decomposition","date":"2019-08-23","arxiv_id":"1908.08628","n_code_links":3,"syntology":{"ran":8,"of":11,"n_ran_checked":8,"n_instrument":0,"unverified":3,"pointer_only":2,"phrase":"8 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; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["lmhieu612/SID"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"a-joint-3d-unet-graph-neural-network-based","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","date":"2019-08-22","arxiv_id":"1908.08588","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-green-function-convolution-for-improving","title":"Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks","date":"2019-08-22","arxiv_id":"1908.08331","n_code_links":0,"syntology":null},{"paper":null,"slug":"feedbackward-decoding-for-semantic","title":"Feedbackward Decoding for Semantic Segmentation","date":"2019-08-22","arxiv_id":"1908.08584","n_code_links":0,"syntology":null},{"paper":"/paper/indoor-depth-completion-with-boundary","slug":"indoor-depth-completion-with-boundary","title":"Indoor Depth Completion with Boundary Consistency and Self-Attention","date":"2019-08-22","arxiv_id":"1908.08344","n_code_links":3,"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":{"repos":["patrickwu2/Depth-Completion","tsunghan-wu/depth-completion"],"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":"/paper/mask-textspotter-an-end-to-end-trainable-2","slug":"mask-textspotter-an-end-to-end-trainable-2","title":"Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes","date":"2019-08-22","arxiv_id":"1908.08207","n_code_links":1,"syntology":null},{"paper":"/paper/molecule-property-prediction-based-on-spatial","slug":"molecule-property-prediction-based-on-spatial","title":"Molecule Property Prediction Based on Spatial Graph Embedding","date":"2019-08-22","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-stream-single-shot-spatial-temporal","title":"Multi-Stream Single Shot Spatial-Temporal Action Detection","date":"2019-08-22","arxiv_id":"1908.08178","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiple-instance-dense-connected-convolution","title":"Multiple instance dense connected convolution neural network for aerial image scene classification","date":"2019-08-22","arxiv_id":"1908.08156","n_code_links":0,"syntology":null},{"paper":"/paper/object-detection-on-aerial-imagery-using","slug":"object-detection-on-aerial-imagery-using","title":"Object detection on aerial imagery using CenterNet","date":"2019-08-22","arxiv_id":"1908.08244","n_code_links":1,"syntology":null},{"paper":null,"slug":"190807748","title":"RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs","date":"2019-08-21","arxiv_id":"1908.07748","n_code_links":0,"syntology":null},{"paper":null,"slug":"190807882","title":"Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection","date":"2019-08-21","arxiv_id":"1908.07882","n_code_links":0,"syntology":null},{"paper":null,"slug":"190807904","title":"Effects of Blur and Deblurring to Visual Object Tracking","date":"2019-08-21","arxiv_id":"1908.07904","n_code_links":0,"syntology":null},{"paper":"/paper/instaboost-boosting-instance-segmentation-via","slug":"instaboost-boosting-instance-segmentation-via","title":"InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting","date":"2019-08-21","arxiv_id":"1908.07801","n_code_links":3,"syntology":null},{"paper":null,"slug":"lung-segmentation-on-chest-x-ray-images-in","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","date":"2019-08-21","arxiv_id":"1908.07704","n_code_links":0,"syntology":null},{"paper":null,"slug":"u-net-training-with-instance-layer","title":"U-Net Training with Instance-Layer Normalization","date":"2019-08-21","arxiv_id":"1908.08466","n_code_links":0,"syntology":null},{"paper":"/paper/190807625","slug":"190807625","title":"Action recognition with spatial-temporal discriminative filter banks","date":"2019-08-20","arxiv_id":"1908.07625","n_code_links":0,"syntology":null},{"paper":"/paper/190807919","slug":"190807919","title":"Deep High-Resolution Representation Learning for Visual Recognition","date":"2019-08-20","arxiv_id":"1908.07919","n_code_links":42,"syntology":{"ran":21,"of":34,"n_ran_checked":19,"n_instrument":2,"unverified":13,"pointer_only":21,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 1 violated, 18 with no contract checked; 2 where Syntology's instrument failed) · 13 unverified","official":null}},{"paper":null,"slug":"a-novel-method-for-idc-prediction-in-breast","title":"A Novel method for IDC Prediction in Breast Cancer Histopathology images using Deep Residual Neural Networks","date":"2019-08-20","arxiv_id":"1908.07362","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-end-to-end-video-text-detector-with-online","title":"An End-to-end Video Text Detector with Online Tracking","date":"2019-08-20","arxiv_id":"1908.07135","n_code_links":0,"syntology":null},{"paper":null,"slug":"consistent-scale-normalization-for-object","title":"Instance Scale Normalization for image understanding","date":"2019-08-20","arxiv_id":"1908.07323","n_code_links":0,"syntology":null},{"paper":"/paper/human-mesh-recovery-from-monocular-images-via","slug":"human-mesh-recovery-from-monocular-images-via","title":"Human Mesh Recovery from Monocular Images via a Skeleton-disentangled Representation","date":"2019-08-20","arxiv_id":"1908.07172","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":["Arthur151/DSD-SATN"],"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/image-synthesis-from-reconfigurable-layout","slug":"image-synthesis-from-reconfigurable-layout","title":"Image Synthesis From Reconfigurable Layout and Style","date":"2019-08-20","arxiv_id":"1908.07500","n_code_links":4,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"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":{"repos":["iVMCL/LostGANs"],"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":"c-rpns-promoting-object-detection-in-real","title":"C-RPNs: Promoting Object Detection in real world via a Cascade Structure of Region Proposal Networks","date":"2019-08-19","arxiv_id":"1908.06665","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-consistent-artifact-reduction-for","title":"Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior","date":"2019-08-19","arxiv_id":"1908.06792","n_code_links":0,"syntology":null},{"paper":"/paper/deep-active-lesion-segmentation","slug":"deep-active-lesion-segmentation","title":"Deep Active Lesion Segmentation","date":"2019-08-19","arxiv_id":"1908.06933","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-weisfeiler-lehman-assignment-kernels-via","title":"Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning","date":"2019-08-19","arxiv_id":"1908.06661","n_code_links":0,"syntology":null},{"paper":"/paper/directionally-constrained-fully-convolutional","slug":"directionally-constrained-fully-convolutional","title":"Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud Classification","date":"2019-08-19","arxiv_id":"1908.06673","n_code_links":1,"syntology":null},{"paper":null,"slug":"fully-automated-image-de-fencing-using","title":"Fully Automated Image De-fencing using Conditional Generative Adversarial Networks","date":"2019-08-19","arxiv_id":"1908.06837","n_code_links":0,"syntology":null},{"paper":null,"slug":"polygan-high-order-polynomial-generators","title":"PolyGAN: High-Order Polynomial Generators","date":"2019-08-19","arxiv_id":"1908.06571","n_code_links":0,"syntology":null},{"paper":null,"slug":"spa-gan-spatial-attention-gan-for-image-to","title":"SPA-GAN: Spatial Attention GAN for Image-to-Image Translation","date":"2019-08-19","arxiv_id":"1908.06616","n_code_links":0,"syntology":null},{"paper":"/paper/a-fast-and-accurate-one-stage-approach-to","slug":"a-fast-and-accurate-one-stage-approach-to","title":"A Fast and Accurate One-Stage Approach to Visual Grounding","date":"2019-08-18","arxiv_id":"1908.06354","n_code_links":2,"syntology":{"ran":16,"of":19,"n_ran_checked":16,"n_instrument":0,"unverified":3,"pointer_only":3,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 1 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["zyang-ur/onestage_grounding"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/panet-few-shot-image-semantic-segmentation","slug":"panet-few-shot-image-semantic-segmentation","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","date":"2019-08-18","arxiv_id":"1908.06391","n_code_links":5,"syntology":null},{"paper":"/paper/vusfavariational-universal-successor-features","slug":"vusfavariational-universal-successor-features","title":"VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation","date":"2019-08-18","arxiv_id":"1908.06376","n_code_links":2,"syntology":null},{"paper":null,"slug":"eigenrank-by-committee-a-data-subset","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","date":"2019-08-17","arxiv_id":"1908.06337","n_code_links":0,"syntology":null},{"paper":"/paper/rotation-invariant-convolutions-for-3d-point","slug":"rotation-invariant-convolutions-for-3d-point","title":"Rotation Invariant Convolutions for 3D Point Clouds Deep Learning","date":"2019-08-17","arxiv_id":"1908.06297","n_code_links":1,"syntology":{"ran":7,"of":11,"n_ran_checked":7,"n_instrument":0,"unverified":4,"pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["hkust-vgd/riconv"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/shellnet-efficient-point-cloud-convolutional","slug":"shellnet-efficient-point-cloud-convolutional","title":"ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics","date":"2019-08-17","arxiv_id":"1908.06295","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-deep-learning-based-surrogate-model-for","title":"A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems","date":"2019-08-16","arxiv_id":"1908.05823","n_code_links":0,"syntology":null},{"paper":null,"slug":"conv2warp-an-unsupervised-deformable-image","title":"Conv2Warp: An unsupervised deformable image registration with continuous convolution and warping","date":"2019-08-16","arxiv_id":"1908.06194","n_code_links":0,"syntology":null},{"paper":null,"slug":"differentiable-learning-to-group-channels","title":"Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks","date":"2019-08-16","arxiv_id":"1908.05867","n_code_links":0,"syntology":null},{"paper":"/paper/fsgan-subject-agnostic-face-swapping-and","slug":"fsgan-subject-agnostic-face-swapping-and","title":"FSGAN: Subject Agnostic Face Swapping and Reenactment","date":"2019-08-16","arxiv_id":"1908.05932","n_code_links":1,"syntology":{"ran":5,"of":8,"n_ran_checked":4,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"5 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; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["YuvalNirkin/fsgan"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"iterative-update-and-unified-representation","title":"Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning","date":"2019-08-16","arxiv_id":"1908.06758","n_code_links":0,"syntology":null},{"paper":"/paper/learning-fixed-points-in-generative","slug":"learning-fixed-points-in-generative","title":"Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization","date":"2019-08-16","arxiv_id":"1908.06965","n_code_links":2,"syntology":null},{"paper":"/paper/occlusion-shared-and-feature-separated","slug":"occlusion-shared-and-feature-separated","title":"Occlusion-shared and Feature-separated Network for Occlusion Relationship Reasoning","date":"2019-08-16","arxiv_id":"1908.05898","n_code_links":1,"syntology":null},{"paper":"/paper/tag2pix-line-art-colorization-using-text-tag","slug":"tag2pix-line-art-colorization-using-text-tag","title":"Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss","date":"2019-08-16","arxiv_id":"1908.05840","n_code_links":2,"syntology":null},{"paper":null,"slug":"the-angel-is-in-the-priors-improving-gan","title":"The Angel is in the Priors: Improving GAN based Image and Sequence Inpainting with Better Noise and Structural Priors","date":"2019-08-16","arxiv_id":"1908.05861","n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerated-cnn-training-through-gradient","title":"Accelerated CNN Training Through Gradient Approximation","date":"2019-08-15","arxiv_id":"1908.05460","n_code_links":0,"syntology":null},{"paper":"/paper/cosmological-n-body-simulations-a-challenge","slug":"cosmological-n-body-simulations-a-challenge","title":"Cosmological N-body simulations: a challenge for scalable generative models","date":"2019-08-15","arxiv_id":"1908.05519","n_code_links":1,"syntology":null},{"paper":null,"slug":"foveated-image-processing-for-faster-object","title":"Foveated image processing for faster object detection and recognition in embedded systems using deep convolutional neural networks","date":"2019-08-15","arxiv_id":"1908.09000","n_code_links":0,"syntology":null},{"paper":null,"slug":"sfsegnet-parse-freehand-sketches-using-deep","title":"SFSegNet: Parse Freehand Sketches using Deep Fully Convolutional Networks","date":"2019-08-15","arxiv_id":"1908.05389","n_code_links":0,"syntology":null},{"paper":null,"slug":"structured-coupled-generative-adversarial","title":"Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation","date":"2019-08-15","arxiv_id":"1908.05794","n_code_links":0,"syntology":null},{"paper":"/paper/adagcn-adaboosting-graph-convolutional","slug":"adagcn-adaboosting-graph-convolutional","title":"AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models","date":"2019-08-14","arxiv_id":"1908.05081","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-detection-and-diagnosis-of","title":"Automatic detection and diagnosis of sacroiliitis in CT scans as incidental findings","date":"2019-08-14","arxiv_id":"1908.05663","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-liver-and-lesion-segmentation-from","title":"Mask Mining for Improved Liver Lesion Segmentation","date":"2019-08-14","arxiv_id":"1908.05062","n_code_links":0,"syntology":null},{"paper":"/paper/conv-mcd-a-plug-and-play-multi-task-module","slug":"conv-mcd-a-plug-and-play-multi-task-module","title":"Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation","date":"2019-08-14","arxiv_id":"1908.05311","n_code_links":1,"syntology":null},{"paper":"/paper/d-unet-a-dimension-fusion-u-shape-network-for","slug":"d-unet-a-dimension-fusion-u-shape-network-for","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","date":"2019-08-14","arxiv_id":"1908.05104","n_code_links":3,"syntology":null},{"paper":null,"slug":"detecting-11k-classes-large-scale-object","title":"Detecting 11K Classes: Large Scale Object Detection without Fine-Grained Bounding Boxes","date":"2019-08-14","arxiv_id":"1908.05217","n_code_links":0,"syntology":null},{"paper":null,"slug":"faster-unsupervised-semantic-inpainting-a-gan","title":"Faster Unsupervised Semantic Inpainting: A GAN Based Approach","date":"2019-08-14","arxiv_id":"1908.04968","n_code_links":0,"syntology":null},{"paper":null,"slug":"histographs-graphs-in-histopathology","title":"Histographs: Graphs in Histopathology","date":"2019-08-14","arxiv_id":"1908.05020","n_code_links":0,"syntology":null},{"paper":"/paper/person-re-identification-in-aerial-imagery","slug":"person-re-identification-in-aerial-imagery","title":"Person Re-identification in Aerial Imagery","date":"2019-08-14","arxiv_id":"1908.05024","n_code_links":1,"syntology":null},{"paper":null,"slug":"segmentation-of-multimodal-myocardial-images","title":"Segmentation of Multimodal Myocardial Images Using Shape-Transfer GAN","date":"2019-08-14","arxiv_id":"1908.05094","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-learning-with-adaptive","title":"Semi-supervised Learning with Adaptive Neighborhood Graph Propagation Network","date":"2019-08-14","arxiv_id":"1908.05153","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosted-gan-with-semantically-interpretable","title":"Boosted GAN with Semantically Interpretable Information for Image Inpainting","date":"2019-08-13","arxiv_id":"1908.04503","n_code_links":0,"syntology":null},{"paper":"/paper/einconv-exploring-unexplored-tensor","slug":"einconv-exploring-unexplored-tensor","title":"Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks","date":"2019-08-13","arxiv_id":"1908.04471","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":0,"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":["pfnet-research/einconv"],"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":null,"slug":"frame-to-frame-aggregation-of-active-regions","title":"Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation","date":"2019-08-13","arxiv_id":"1908.04501","n_code_links":0,"syntology":null},{"paper":"/paper/interpolated-convolutional-networks-for-3d","slug":"interpolated-convolutional-networks-for-3d","title":"Interpolated Convolutional Networks for 3D Point Cloud Understanding","date":"2019-08-13","arxiv_id":"1908.04512","n_code_links":0,"syntology":null},{"paper":"/paper/matrix-nets-a-new-deep-architecture-for","slug":"matrix-nets-a-new-deep-architecture-for","title":"Matrix Nets: A New Deep Architecture for Object Detection","date":"2019-08-13","arxiv_id":"1908.04646","n_code_links":2,"syntology":null},{"paper":null,"slug":"automated-brain-tumour-segmentation-using","title":"Automated Brain Tumour Segmentation Using Deep Fully Residual Convolutional Neural Networks","date":"2019-08-12","arxiv_id":"1908.04250","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-region-division-for-adaptive-learning","title":"Dynamic Region Division for Adaptive Learning Pedestrian Counting","date":"2019-08-12","arxiv_id":"1908.03978","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhanced-3d-convolutional-networks-for-crowd","title":"Enhanced 3D convolutional networks for crowd counting","date":"2019-08-12","arxiv_id":"1908.04121","n_code_links":0,"syntology":null},{"paper":"/paper/explicit-shape-encoding-for-real-time","slug":"explicit-shape-encoding-for-real-time","title":"Explicit Shape Encoding for Real-Time Instance Segmentation","date":"2019-08-12","arxiv_id":"1908.04067","n_code_links":1,"syntology":null},{"paper":"/paper/lip-local-importance-based-pooling","slug":"lip-local-importance-based-pooling","title":"LIP: Local Importance-based Pooling","date":"2019-08-12","arxiv_id":"1908.04156","n_code_links":1,"syntology":null},{"paper":"/paper/mulan-multitask-universal-lesion-analysis","slug":"mulan-multitask-universal-lesion-analysis","title":"MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation","date":"2019-08-12","arxiv_id":"1908.04373","n_code_links":15,"syntology":null},{"paper":"/paper/acnet-strengthening-the-kernel-skeletons-for","slug":"acnet-strengthening-the-kernel-skeletons-for","title":"ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks","date":"2019-08-11","arxiv_id":"1908.03930","n_code_links":5,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"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) · 2 unverified","official":{"repos":["ShawnDing1994/ACNet"],"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":"cmb-gan-fast-simulations-of-cosmic-microwave","title":"CMB-GAN: Fast Simulations of Cosmic Microwave background anisotropy maps using Deep Learning","date":"2019-08-11","arxiv_id":"1908.04682","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-structurally-strengthened","title":"Efficient Structurally-Strengthened Generative Adversarial Network for MRI Reconstruction","date":"2019-08-11","arxiv_id":"1908.03858","n_code_links":0,"syntology":null},{"paper":"/paper/hbonet-harmonious-bottleneck-on-two","slug":"hbonet-harmonious-bottleneck-on-two","title":"HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions","date":"2019-08-11","arxiv_id":"1908.03888","n_code_links":1,"syntology":null},{"paper":null,"slug":"mobilefan-transferring-deep-hidden","title":"MobileFAN: Transferring Deep Hidden Representation for Face Alignment","date":"2019-08-11","arxiv_id":"1908.03839","n_code_links":0,"syntology":null},{"paper":null,"slug":"specae-spectral-autoencoder-for-anomaly","title":"SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks","date":"2019-08-11","arxiv_id":"1908.03849","n_code_links":0,"syntology":null},{"paper":"/paper/censnet-convolution-with-edge-node-switching","slug":"censnet-convolution-with-edge-node-switching","title":"CensNet: Convolution with Edge-Node Switching in Graph Neural Networks","date":"2019-08-10","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/channel-decomposition-on-generative-networks","slug":"channel-decomposition-on-generative-networks","title":"Channel Decomposition into Painting Actions","date":"2019-08-10","arxiv_id":"1908.04694","n_code_links":1,"syntology":null},{"paper":"/paper/deblurgan-v2-deblurring-orders-of-magnitude","slug":"deblurgan-v2-deblurring-orders-of-magnitude","title":"DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better","date":"2019-08-10","arxiv_id":"1908.03826","n_code_links":6,"syntology":null},{"paper":null,"slug":"semi-supervised-multi-task-learning-with","title":"Semi-Supervised Multi-Task Learning With Chest X-Ray Images","date":"2019-08-10","arxiv_id":"1908.03693","n_code_links":0,"syntology":null}],"record_sha256":"1a4b5179839f61ad1aed2fb9598e90d146b3c6191d7e7ede9b2a57f866f539f9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}