{"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/112","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":112,"pages_in_order":196,"rows_per_page":100,"rows":[11101,11200],"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/111","next":"/method/convolution/papers/113","papers":[{"paper":null,"slug":"scalable-visual-attribute-extraction-through","title":"Scalable Visual Attribute Extraction through Hidden Layers of a Residual ConvNet","date":"2021-03-31","arxiv_id":"2104.00161","n_code_links":0,"syntology":null},{"paper":"/paper/scale-aware-automatic-augmentation-for-object","slug":"scale-aware-automatic-augmentation-for-object","title":"Scale-aware Automatic Augmentation for Object Detection","date":"2021-03-31","arxiv_id":"2103.17220","n_code_links":1,"syntology":null},{"paper":null,"slug":"self-regression-learning-for-blind","title":"Self-Regression Learning for Blind Hyperspectral Image Fusion Without Label","date":"2021-03-31","arxiv_id":"2103.16806","n_code_links":0,"syntology":null},{"paper":"/paper/styleclip-text-driven-manipulation-of","slug":"styleclip-text-driven-manipulation-of","title":"StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery","date":"2021-03-31","arxiv_id":"2103.17249","n_code_links":5,"syntology":{"ran":7,"of":11,"n_ran_checked":6,"n_instrument":1,"unverified":4,"pointer_only":1,"phrase":"7 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["orpatashnik/StyleCLIP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"using-depth-information-and-colour-space","title":"Using depth information and colour space variations for improving outdoor robustness for instance segmentation of cabbage","date":"2021-03-31","arxiv_id":"2103.16923","n_code_links":0,"syntology":null},{"paper":"/paper/automated-cleanup-of-the-imagenet-dataset-by","slug":"automated-cleanup-of-the-imagenet-dataset-by","title":"Automated Cleanup of the ImageNet Dataset by Model Consensus, Explainability and Confident Learning","date":"2021-03-30","arxiv_id":"2103.16324","n_code_links":1,"syntology":null},{"paper":"/paper/automatic-airway-segmentation-from-computed","slug":"automatic-airway-segmentation-from-computed","title":"Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networks","date":"2021-03-30","arxiv_id":"2103.16328","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-graph-partitioning-for-very-large","title":"Automatic Graph Partitioning for Very Large-scale Deep Learning","date":"2021-03-30","arxiv_id":"2103.16063","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-representation-learning-for","slug":"benchmarking-representation-learning-for","title":"Benchmarking Representation Learning for Natural World Image Collections","date":"2021-03-30","arxiv_id":"2103.16483","n_code_links":1,"syntology":{"ran":7,"of":12,"n_ran_checked":7,"n_instrument":0,"unverified":5,"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) · 5 unverified","official":{"repos":["visipedia/newt"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"cuconv-a-cuda-implementation-of-convolution","title":"cuConv: A CUDA Implementation of Convolution for CNN Inference","date":"2021-03-30","arxiv_id":"2103.16234","n_code_links":0,"syntology":null},{"paper":null,"slug":"deepword-a-gcn-based-approach-for-owner","title":"DeepWORD: A GCN-based Approach for Owner-Member Relationship Detection in Autonomous Driving","date":"2021-03-30","arxiv_id":"2103.16099","n_code_links":0,"syntology":null},{"paper":"/paper/diagonal-attention-and-style-based-gan-for","slug":"diagonal-attention-and-style-based-gan-for","title":"Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and Translation","date":"2021-03-30","arxiv_id":"2103.16146","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 1 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; the one sample that ran constructed an object rather than computing a result","official":null}},{"paper":null,"slug":"differentiable-network-adaption-with-elastic","title":"Differentiable Network Adaption with Elastic Search Space","date":"2021-03-30","arxiv_id":"2103.16350","n_code_links":0,"syntology":null},{"paper":null,"slug":"enabling-homomorphically-encrypted-inference","title":"Enabling Homomorphically Encrypted Inference for Large DNN Models","date":"2021-03-30","arxiv_id":"2103.16139","n_code_links":0,"syntology":null},{"paper":"/paper/exploiting-invariance-in-training-deep-neural","slug":"exploiting-invariance-in-training-deep-neural","title":"Exploiting Invariance in Training Deep Neural Networks","date":"2021-03-30","arxiv_id":"2103.16634","n_code_links":1,"syntology":null},{"paper":"/paper/fully-convolutional-scene-graph-generation","slug":"fully-convolutional-scene-graph-generation","title":"Fully Convolutional Scene Graph Generation","date":"2021-03-30","arxiv_id":"2103.16083","n_code_links":1,"syntology":{"ran":12,"of":12,"n_ran_checked":10,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"12 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["liuhengyue/fcsgg"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"identity-aware-cyclegan-for-face-photo-sketch","title":"Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition","date":"2021-03-30","arxiv_id":"2103.16019","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-robustness-against-common-1","title":"Improving robustness against common corruptions with frequency biased models","date":"2021-03-30","arxiv_id":"2103.16241","n_code_links":0,"syntology":null},{"paper":null,"slug":"nonlinear-weighted-directed-acyclic-graph-and","title":"Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks","date":"2021-03-30","arxiv_id":"2103.16355","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-disentanglement-of-linear","title":"Unsupervised Disentanglement of Linear-Encoded Facial Semantics","date":"2021-03-30","arxiv_id":"2103.16605","n_code_links":0,"syntology":null},{"paper":"/paper/2103-15358","slug":"2103-15358","title":"Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding","date":"2021-03-29","arxiv_id":"2103.15358","n_code_links":3,"syntology":{"ran":8,"of":10,"n_ran_checked":5,"n_instrument":3,"unverified":2,"pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 2 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":{"repos":["microsoft/vision-longformer"],"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":["listed","official"]}}},{"paper":null,"slug":"automating-defense-against-adversarial","title":"Automating Defense Against Adversarial Attacks: Discovery of Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed Models","date":"2021-03-29","arxiv_id":"2103.15897","n_code_links":0,"syntology":null},{"paper":null,"slug":"classification-of-seeds-using-domain","title":"Classification of Seeds using Domain Randomization on Self-Supervised Learning Frameworks","date":"2021-03-29","arxiv_id":"2103.15578","n_code_links":0,"syntology":null},{"paper":"/paper/cvt-introducing-convolutions-to-vision","slug":"cvt-introducing-convolutions-to-vision","title":"CvT: Introducing Convolutions to Vision Transformers","date":"2021-03-29","arxiv_id":"2103.15808","n_code_links":16,"syntology":{"ran":39,"of":47,"n_ran_checked":36,"n_instrument":3,"unverified":8,"pointer_only":8,"phrase":"39 ran (of which 19 constructed an object rather than computing a result; 36 with no instrument failure: 2 honoured, 0 violated, 34 with no contract checked; 3 where Syntology's instrument failed) · 8 unverified","official":{"repos":["microsoft/CvT"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":4,"ran_from_kinds":["listed","named_in_paper","official","unlocated"]}}},{"paper":"/paper/enhanced-boundary-learning-for-glass-like","slug":"enhanced-boundary-learning-for-glass-like","title":"Enhanced Boundary Learning for Glass-like Object Segmentation","date":"2021-03-29","arxiv_id":"2103.15734","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":2,"n_instrument":2,"unverified":0,"pointer_only":4,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["hehao13/EBLNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"fixnorm-dissecting-weight-decay-for-training-1","title":"FixNorm: Dissecting Weight Decay for Training Deep Neural Networks","date":"2021-03-29","arxiv_id":"2103.15345","n_code_links":0,"syntology":null},{"paper":null,"slug":"generalizing-to-the-open-world-deep-visual","title":"Generalizing to the Open World: Deep Visual Odometry with Online Adaptation","date":"2021-03-29","arxiv_id":"2103.15279","n_code_links":0,"syntology":null},{"paper":null,"slug":"industry-scale-semi-supervised-learning-for","title":"Industry Scale Semi-Supervised Learning for Natural Language Understanding","date":"2021-03-29","arxiv_id":"2103.15871","n_code_links":0,"syntology":null},{"paper":"/paper/ran-gnns-breaking-the-capacity-limits-of","slug":"ran-gnns-breaking-the-capacity-limits-of","title":"RAN-GNNs: breaking the capacity limits of graph neural networks","date":"2021-03-29","arxiv_id":"2103.15565","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-neural-operations-for-diverse","slug":"rethinking-neural-operations-for-diverse","title":"Rethinking Neural Operations for Diverse Tasks","date":"2021-03-29","arxiv_id":"2103.15798","n_code_links":3,"syntology":{"ran":22,"of":34,"n_ran_checked":17,"n_instrument":5,"unverified":12,"pointer_only":1,"phrase":"22 ran (of which 8 constructed an object rather than computing a result; 17 with no instrument failure: 3 honoured, 0 violated, 14 with no contract checked; 5 where Syntology's instrument failed) · 12 unverified","official":{"repos":["mkhodak/relax","nick11roberts/XD"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":8,"n_ran_no_instrument_failure":17,"n_unverified":11,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"translating-numerical-concepts-for-pdes-into","title":"Translating Numerical Concepts for PDEs into Neural Architectures","date":"2021-03-29","arxiv_id":"2103.15419","n_code_links":0,"syntology":null},{"paper":null,"slug":"bcnn-binary-complex-neural-network","title":"BCNN: Binary Complex Neural Network","date":"2021-03-28","arxiv_id":"2104.10044","n_code_links":0,"syntology":null},{"paper":null,"slug":"rethinking-resnets-improved-stacking","title":"Rethinking ResNets: Improved Stacking Strategies With High Order Schemes","date":"2021-03-28","arxiv_id":"2103.15244","n_code_links":0,"syntology":null},{"paper":"/paper/2103-14862","slug":"2103-14862","title":"TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization","date":"2021-03-27","arxiv_id":"2103.14862","n_code_links":2,"syntology":null},{"paper":"/paper/covid-19-personal-protective-equipment","slug":"covid-19-personal-protective-equipment","title":"COVID-19 personal protective equipment detection using real-time deep learning methods","date":"2021-03-27","arxiv_id":"2103.14878","n_code_links":1,"syntology":null},{"paper":"/paper/few-shot-semantic-image-synthesis-using","slug":"few-shot-semantic-image-synthesis-using","title":"Few-shot Semantic Image Synthesis Using StyleGAN Prior","date":"2021-03-27","arxiv_id":"2103.14877","n_code_links":1,"syntology":null},{"paper":null,"slug":"instance-segmentation-with-the-number-of","title":"Instance segmentation with the number of clusters incorporated in embedding learning","date":"2021-03-27","arxiv_id":"2103.14869","n_code_links":0,"syntology":null},{"paper":"/paper/labels4free-unsupervised-segmentation-using","slug":"labels4free-unsupervised-segmentation-using","title":"Labels4Free: Unsupervised Segmentation using StyleGAN","date":"2021-03-27","arxiv_id":"2103.14968","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["RameenAbdal/Labels4Free"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"representation-analysis-of-bayesian","title":"Representation, Analysis of Bayesian Refinement Approximation Network: A Survey","date":"2021-03-27","arxiv_id":"2103.14896","n_code_links":0,"syntology":null},{"paper":"/paper/self-adaptive-torque-vectoring-controller","slug":"self-adaptive-torque-vectoring-controller","title":"Self-adaptive Torque Vectoring Controller Using Reinforcement Learning","date":"2021-03-27","arxiv_id":"2103.14892","n_code_links":1,"syntology":null},{"paper":"/paper/3d-point-cloud-registration-with-multi-scale","slug":"3d-point-cloud-registration-with-multi-scale","title":"3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning","date":"2021-03-26","arxiv_id":"2103.14533","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-practical-survey-on-faster-and-lighter","title":"A Practical Survey on Faster and Lighter Transformers","date":"2021-03-26","arxiv_id":"2103.14636","n_code_links":0,"syntology":null},{"paper":"/paper/automated-radiology-report-generation-using","slug":"automated-radiology-report-generation-using","title":"Automated radiology report generation using conditioned transformers","date":"2021-03-26","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"detection-growth-quantification-and","title":"Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans","date":"2021-03-26","arxiv_id":"2103.14537","n_code_links":0,"syntology":null},{"paper":"/paper/distilling-object-detectors-via-decoupled","slug":"distilling-object-detectors-via-decoupled","title":"Distilling Object Detectors via Decoupled Features","date":"2021-03-26","arxiv_id":"2103.14475","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluation-of-preprocessing-techniques-for-u","title":"Evaluation of Preprocessing Techniques for U-Net Based Automated Liver Segmentation","date":"2021-03-26","arxiv_id":"2103.14301","n_code_links":0,"syntology":null},{"paper":null,"slug":"image-desaturation-for-sdo-aia-using-deep","title":"Image Desaturation for SDO/AIA Using Deep Learning","date":"2021-03-26","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"improve-gan-based-neural-vocoder-using","title":"Improve GAN-based Neural Vocoder using Pointwise Relativistic LeastSquare GAN","date":"2021-03-26","arxiv_id":"2103.14245","n_code_links":0,"syntology":null},{"paper":"/paper/on-generating-transferable-targeted","slug":"on-generating-transferable-targeted","title":"On Generating Transferable Targeted Perturbations","date":"2021-03-26","arxiv_id":"2103.14641","n_code_links":3,"syntology":{"ran":5,"of":5,"n_ran_checked":2,"n_instrument":3,"unverified":0,"pointer_only":2,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["Muzammal-Naseer/TTP"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"online-structural-health-monitoring-by-model","title":"Online structural health monitoring by model order reduction and deep learning algorithms","date":"2021-03-26","arxiv_id":"2103.14328","n_code_links":0,"syntology":null},{"paper":"/paper/paconv-position-adaptive-convolution-with","slug":"paconv-position-adaptive-convolution-with","title":"PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds","date":"2021-03-26","arxiv_id":"2103.14635","n_code_links":2,"syntology":{"ran":4,"of":5,"n_ran_checked":3,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["CVMI-Lab/PAConv"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"training-a-better-loss-function-for-image","title":"Training a Task-Specific Image Reconstruction Loss","date":"2021-03-26","arxiv_id":"2103.14616","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-robustness-of-transformers-for","title":"Understanding Robustness of Transformers for Image Classification","date":"2021-03-26","arxiv_id":"2103.14586","n_code_links":0,"syntology":null},{"paper":"/paper/an-image-is-worth-16x16-words-what-is-a-video","slug":"an-image-is-worth-16x16-words-what-is-a-video","title":"An Image is Worth 16x16 Words, What is a Video Worth?","date":"2021-03-25","arxiv_id":"2103.13915","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":{"repos":["Alibaba-MIIL/STAM"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/asymmetric-cnn-for-image-super-resolution","slug":"asymmetric-cnn-for-image-super-resolution","title":"Asymmetric CNN for image super-resolution","date":"2021-03-25","arxiv_id":"2103.13634","n_code_links":1,"syntology":null},{"paper":"/paper/convolutional-neural-network-hyperparameters","slug":"convolutional-neural-network-hyperparameters","title":"Convolutional Neural Network Hyperparameters optimization for Facial Emotion Recognition","date":"2021-03-25","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"deep-air-a-hybrid-cnn-lstm-framework-for-air","title":"Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Cities","date":"2021-03-25","arxiv_id":"2103.14587","n_code_links":0,"syntology":null},{"paper":"/paper/efficienttdnn-efficient-architecture-search","slug":"efficienttdnn-efficient-architecture-search","title":"EfficientTDNN: Efficient Architecture Search for Speaker Recognition","date":"2021-03-25","arxiv_id":"2103.13581","n_code_links":1,"syntology":null},{"paper":"/paper/equivariant-point-network-for-3d-point-cloud","slug":"equivariant-point-network-for-3d-point-cloud","title":"Equivariant Point Network for 3D Point Cloud Analysis","date":"2021-03-25","arxiv_id":"2103.14147","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["nintendops/EPN_PointCloud"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/evidential-fully-convolutional-network-for","slug":"evidential-fully-convolutional-network-for","title":"Evidential fully convolutional network for semantic segmentation","date":"2021-03-25","arxiv_id":"2103.13544","n_code_links":1,"syntology":null},{"paper":null,"slug":"inversionnet3d-efficient-and-scalable","title":"InversionNet3D: Efficient and Scalable Learning for 3D Full Waveform Inversion","date":"2021-03-25","arxiv_id":"2103.14158","n_code_links":0,"syntology":null},{"paper":"/paper/subspectral-normalization-for-neural-audio","slug":"subspectral-normalization-for-neural-audio","title":"SubSpectral Normalization for Neural Audio Data Processing","date":"2021-03-25","arxiv_id":"2103.13620","n_code_links":0,"syntology":null},{"paper":"/paper/usb-universal-scale-object-detection","slug":"usb-universal-scale-object-detection","title":"USB: Universal-Scale Object Detection Benchmark","date":"2021-03-25","arxiv_id":"2103.14027","n_code_links":1,"syntology":null},{"paper":"/paper/video-instance-segmentation-with-a-propose","slug":"video-instance-segmentation-with-a-propose","title":"Video Instance Segmentation with a Propose-Reduce Paradigm","date":"2021-03-25","arxiv_id":"2103.13746","n_code_links":1,"syntology":{"ran":2,"of":5,"n_ran_checked":2,"n_instrument":0,"unverified":3,"pointer_only":5,"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) · 3 unverified","official":{"repos":["dvlab-research/proposereduce"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-framework-for-3d-tracking-of-frontal","title":"A Framework for 3D Tracking of Frontal Dynamic Objects in Autonomous Cars","date":"2021-03-24","arxiv_id":"2103.13430","n_code_links":0,"syntology":null},{"paper":"/paper/diverse-branch-block-building-a-convolution","slug":"diverse-branch-block-building-a-convolution","title":"Diverse Branch Block: Building a Convolution as an Inception-like Unit","date":"2021-03-24","arxiv_id":"2103.13425","n_code_links":3,"syntology":{"ran":11,"of":20,"n_ran_checked":4,"n_instrument":7,"unverified":9,"pointer_only":6,"phrase":"11 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 7 where Syntology's instrument failed) · 9 unverified","official":{"repos":["DingXiaoH/DiverseBranchBlock"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/efficient-multi-objective-optimization-for","slug":"efficient-multi-objective-optimization-for","title":"Scalable Pareto Front Approximation for Deep Multi-Objective Learning","date":"2021-03-24","arxiv_id":"2103.13392","n_code_links":1,"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":["ruchtem/cosmos"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"learning-fine-grained-segmentation-of-3d","title":"Learning Fine-Grained Segmentation of 3D Shapes without Part Labels","date":"2021-03-24","arxiv_id":"2103.13030","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-based-automatic-graphene","title":"Machine Learning-based Automatic Graphene Detection with Color Correction for Optical Microscope Images","date":"2021-03-24","arxiv_id":"2103.13495","n_code_links":0,"syntology":null},{"paper":null,"slug":"manas-multi-scale-and-multi-level-neural","title":"MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising","date":"2021-03-24","arxiv_id":"2103.12995","n_code_links":0,"syntology":null},{"paper":"/paper/matched-sample-selection-with-gans-for","slug":"matched-sample-selection-with-gans-for","title":"Matched sample selection with GANs for mitigating attribute confounding","date":"2021-03-24","arxiv_id":"2103.13455","n_code_links":1,"syntology":null},{"paper":"/paper/modgnn-expert-policy-approximation-in-multi","slug":"modgnn-expert-policy-approximation-in-multi","title":"ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture","date":"2021-03-24","arxiv_id":"2103.13446","n_code_links":1,"syntology":null},{"paper":null,"slug":"mscfnet-a-lightweight-network-with-multi","title":"MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation","date":"2021-03-24","arxiv_id":"2103.13044","n_code_links":0,"syntology":null},{"paper":"/paper/non-compression-auto-encoder-for-detecting","slug":"non-compression-auto-encoder-for-detecting","title":"Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise","date":"2021-03-24","arxiv_id":"2103.12992","n_code_links":1,"syntology":null},{"paper":"/paper/shift-and-balance-attention","slug":"shift-and-balance-attention","title":"Shift-and-Balance Attention","date":"2021-03-24","arxiv_id":"2103.13080","n_code_links":1,"syntology":null},{"paper":"/paper/vision-transformers-for-dense-prediction","slug":"vision-transformers-for-dense-prediction","title":"Vision Transformers for Dense Prediction","date":"2021-03-24","arxiv_id":"2103.13413","n_code_links":15,"syntology":{"ran":65,"of":116,"n_ran_checked":32,"n_instrument":33,"unverified":51,"pointer_only":15,"phrase":"65 ran (of which 19 constructed an object rather than computing a result; 32 with no instrument failure: 0 honoured, 0 violated, 32 with no contract checked; 33 where Syntology's instrument failed) · 51 unverified","official":null}},{"paper":null,"slug":"are-all-outliers-alike-on-understanding-the-1","title":"Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs","date":"2021-03-23","arxiv_id":"2103.12628","n_code_links":0,"syntology":null},{"paper":"/paper/bossnas-exploring-hybrid-cnn-transformers","slug":"bossnas-exploring-hybrid-cnn-transformers","title":"BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search","date":"2021-03-23","arxiv_id":"2103.12424","n_code_links":1,"syntology":null},{"paper":"/paper/deep-learning-for-fully-automatic-detection","slug":"deep-learning-for-fully-automatic-detection","title":"Deep Learning for fully automatic detection, segmentation, and Gleason Grade estimation of prostate cancer in multiparametric Magnetic Resonance Images","date":"2021-03-23","arxiv_id":"2103.12650","n_code_links":1,"syntology":null},{"paper":null,"slug":"detecting-micro-fractures-with-x-ray-computed","title":"Detecting micro fractures: A comprehensive comparison of conventional and machine-learning based segmentation methods","date":"2021-03-23","arxiv_id":"2103.12821","n_code_links":0,"syntology":null},{"paper":"/paper/dilated-spinenet-for-semantic-segmentation","slug":"dilated-spinenet-for-semantic-segmentation","title":"Dilated SpineNet for Semantic Segmentation","date":"2021-03-23","arxiv_id":"2103.12270","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-deep-learning-pipelines-for","title":"Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload","date":"2021-03-23","arxiv_id":"2103.12465","n_code_links":0,"syntology":null},{"paper":null,"slug":"generalizing-face-forgery-detection-with-high","title":"Generalizing Face Forgery Detection with High-frequency Features","date":"2021-03-23","arxiv_id":"2103.12376","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-comprehensive-motion-representation","title":"Learning Comprehensive Motion Representation for Action Recognition","date":"2021-03-23","arxiv_id":"2103.12278","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-multi-domain-heterogeneous-data","title":"Leveraging Multi-domain, Heterogeneous Data using Deep Multitask Learning for Hate Speech Detection","date":"2021-03-23","arxiv_id":"2103.12412","n_code_links":0,"syntology":null},{"paper":"/paper/rpattack-refined-patch-attack-on-general","slug":"rpattack-refined-patch-attack-on-general","title":"RPATTACK: Refined Patch Attack on General Object Detectors","date":"2021-03-23","arxiv_id":"2103.12469","n_code_links":1,"syntology":null},{"paper":"/paper/watermark-faker-towards-forgery-of-digital","slug":"watermark-faker-towards-forgery-of-digital","title":"Watermark Faker: Towards Forgery of Digital Image Watermarking","date":"2021-03-23","arxiv_id":"2103.12489","n_code_links":1,"syntology":null},{"paper":"/paper/a-batch-normalization-classifier-for-domain","slug":"a-batch-normalization-classifier-for-domain","title":"A Batch Normalization Classifier for Domain Adaptation","date":"2021-03-22","arxiv_id":"2103.11642","n_code_links":1,"syntology":null},{"paper":"/paper/anchor-free-person-search","slug":"anchor-free-person-search","title":"Anchor-Free Person Search","date":"2021-03-22","arxiv_id":"2103.11617","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["daodaofr/AlignPS"],"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/cfpnet-channel-wise-feature-pyramid-for-real","slug":"cfpnet-channel-wise-feature-pyramid-for-real","title":"CFPNet: Channel-wise Feature Pyramid for Real-Time Semantic Segmentation","date":"2021-03-22","arxiv_id":"2103.12212","n_code_links":2,"syntology":null},{"paper":"/paper/control-distance-iou-and-control-distance-iou","slug":"control-distance-iou-and-control-distance-iou","title":"Control Distance IoU and Control Distance IoU Loss Function for Better Bounding Box Regression","date":"2021-03-22","arxiv_id":"2103.11696","n_code_links":1,"syntology":null},{"paper":"/paper/deepvit-towards-deeper-vision-transformer","slug":"deepvit-towards-deeper-vision-transformer","title":"DeepViT: Towards Deeper Vision Transformer","date":"2021-03-22","arxiv_id":"2103.11886","n_code_links":5,"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":{"repos":["zhoudaquan/dvit_repo"],"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":null,"slug":"fast-approximate-spectral-normalization-for","title":"Fast Approximate Spectral Normalization for Robust Deep Neural Networks","date":"2021-03-22","arxiv_id":"2103.13815","n_code_links":0,"syntology":null},{"paper":null,"slug":"hardware-acceleration-of-explainable-machine","title":"Hardware Acceleration of Explainable Machine Learning using Tensor Processing Units","date":"2021-03-22","arxiv_id":"2103.11927","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-motion-video-super-resolution-with-dual","title":"Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling","date":"2021-03-22","arxiv_id":"2103.11744","n_code_links":0,"syntology":null},{"paper":"/paper/meta-detr-few-shot-object-detection-via","slug":"meta-detr-few-shot-object-detection-via","title":"Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation","date":"2021-03-22","arxiv_id":"2103.11731","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":2,"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":["ZhangGongjie/Meta-DETR"],"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/predicting-brain-age-from-raw-t-1-weighted","slug":"predicting-brain-age-from-raw-t-1-weighted","title":"Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks","date":"2021-03-22","arxiv_id":"2103.11695","n_code_links":1,"syntology":null},{"paper":null,"slug":"prediction-of-lung-and-colon-cancer-through","title":"Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps","date":"2021-03-22","arxiv_id":"2103.12155","n_code_links":0,"syntology":null},{"paper":"/paper/prioritycut-occlusion-guided-regularization","slug":"prioritycut-occlusion-guided-regularization","title":"PriorityCut: Occlusion-guided Regularization for Warp-based Image Animation","date":"2021-03-22","arxiv_id":"2103.11600","n_code_links":1,"syntology":null},{"paper":null,"slug":"spatially-dependent-u-nets-highly-accurate","title":"Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation","date":"2021-03-22","arxiv_id":"2103.11713","n_code_links":0,"syntology":null}],"record_sha256":"942544921ecb811ef07b8177902d8bc361c9a3c1017ffb5c5667602ca14e9201","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}