{"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/162","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":162,"pages_in_order":196,"rows_per_page":100,"rows":[16101,16200],"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/161","next":"/method/convolution/papers/163","papers":[{"paper":null,"slug":"magnetoresistive-ram-for-error-resilient-xnor","title":"Magnetoresistive RAM for error resilient XNOR-Nets","date":"2019-05-24","arxiv_id":"1905.10927","n_code_links":0,"syntology":null},{"paper":"/paper/robust-learning-with-implicit-residual","slug":"robust-learning-with-implicit-residual","title":"Robust learning with implicit residual networks","date":"2019-05-24","arxiv_id":"1905.10479","n_code_links":1,"syntology":{"ran":10,"of":16,"n_ran_checked":10,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"10 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; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["vreshniak/ImplicitResNet"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":"/paper/semi-supervised-classification-on-non-sparse","slug":"semi-supervised-classification-on-non-sparse","title":"Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks","date":"2019-05-24","arxiv_id":"1905.10224","n_code_links":2,"syntology":null},{"paper":null,"slug":"semi-supervised-gan-for-classification-of","title":"Semi-supervised GAN for Classification of Multispectral Imagery Acquired by UAVs","date":"2019-05-24","arxiv_id":"1905.10920","n_code_links":0,"syntology":null},{"paper":null,"slug":"structured-compression-by-unstructured","title":"Structured Compression by Weight Encryption for Unstructured Pruning and Quantization","date":"2019-05-24","arxiv_id":"1905.10138","n_code_links":0,"syntology":null},{"paper":null,"slug":"training-decision-trees-as-replacement-for","title":"Training Decision Trees as Replacement for Convolution Layers","date":"2019-05-24","arxiv_id":"1905.10073","n_code_links":0,"syntology":null},{"paper":null,"slug":"what-can-resnet-learn-efficiently-going","title":"What Can ResNet Learn Efficiently, Going Beyond Kernels?","date":"2019-05-24","arxiv_id":"1905.10337","n_code_links":0,"syntology":null},{"paper":null,"slug":"190513300","title":"Generative Imaging and Image Processing via Generative Encoder","date":"2019-05-23","arxiv_id":"1905.13300","n_code_links":0,"syntology":null},{"paper":null,"slug":"decentralized-learning-of-generative","title":"Decentralized Learning of Generative Adversarial Networks from Non-iid Data","date":"2019-05-23","arxiv_id":"1905.09684","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-q-learning-with-q-matrix-transfer","title":"Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment","date":"2019-05-23","arxiv_id":"1905.09673","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-spectrograms-with-convolutional","title":"Learning spectrograms with convolutional spectral kernels","date":"2019-05-23","arxiv_id":"1905.09917","n_code_links":0,"syntology":null},{"paper":"/paper/mr-gnn-multi-resolution-and-dual-graph-neural","slug":"mr-gnn-multi-resolution-and-dual-graph-neural","title":"MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions","date":"2019-05-23","arxiv_id":"1905.09558","n_code_links":2,"syntology":null},{"paper":"/paper/multi-sample-dropout-for-accelerated-training","slug":"multi-sample-dropout-for-accelerated-training","title":"Multi-Sample Dropout for Accelerated Training and Better Generalization","date":"2019-05-23","arxiv_id":"1905.09788","n_code_links":6,"syntology":{"ran":7,"of":7,"n_ran_checked":6,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/network-pruning-via-transformable","slug":"network-pruning-via-transformable","title":"Network Pruning via Transformable Architecture Search","date":"2019-05-23","arxiv_id":"1905.09717","n_code_links":4,"syntology":null},{"paper":"/paper/phom-gem-persistent-homology-for-generative","slug":"phom-gem-persistent-homology-for-generative","title":"PHom-GeM: Persistent Homology for Generative Models","date":"2019-05-23","arxiv_id":"1905.09894","n_code_links":1,"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":["dagrate/phomgem"],"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":"shift-r-cnn-deep-monocular-3d-object","title":"Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints","date":"2019-05-23","arxiv_id":"1905.09970","n_code_links":0,"syntology":null},{"paper":"/paper/spatial-group-wise-enhance-improving-semantic","slug":"spatial-group-wise-enhance-improving-semantic","title":"Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks","date":"2019-05-23","arxiv_id":"1905.09646","n_code_links":3,"syntology":{"ran":2,"of":4,"n_ran_checked":0,"n_instrument":2,"unverified":2,"pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["implus/PytorchInsight"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/the-convolutional-tsetlin-machine","slug":"the-convolutional-tsetlin-machine","title":"The Convolutional Tsetlin Machine","date":"2019-05-23","arxiv_id":"1905.09688","n_code_links":9,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["cair/convolutional-tsetlin-machine","cair/pyTsetlinMachineParallel"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":"/paper/training-language-gans-from-scratch","slug":"training-language-gans-from-scratch","title":"Training language GANs from Scratch","date":"2019-05-23","arxiv_id":"1905.09922","n_code_links":6,"syntology":null},{"paper":"/paper/data-efficient-image-recognition-with","slug":"data-efficient-image-recognition-with","title":"Data-Efficient Image Recognition with Contrastive Predictive Coding","date":"2019-05-22","arxiv_id":"1905.09272","n_code_links":4,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/fastspeech-fast-robust-and-controllable-text","slug":"fastspeech-fast-robust-and-controllable-text","title":"FastSpeech: Fast, Robust and Controllable Text to Speech","date":"2019-05-22","arxiv_id":"1905.09263","n_code_links":22,"syntology":{"ran":10,"of":11,"n_ran_checked":7,"n_instrument":3,"unverified":1,"pointer_only":3,"phrase":"10 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; 3 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/fastspeech-fastrobustand-controllable-text-to","slug":"fastspeech-fastrobustand-controllable-text-to","title":"FastSpeech: Fast,Robustand Controllable Text-to-Speech","date":"2019-05-22","arxiv_id":null,"n_code_links":11,"syntology":null},{"paper":null,"slug":"pepsi-fast-and-lightweight-network-for-image","title":"PEPSI++: Fast and Lightweight Network for Image Inpainting","date":"2019-05-22","arxiv_id":"1905.09010","n_code_links":0,"syntology":null},{"paper":null,"slug":"underwater-color-restoration-using-u-net","title":"Underwater Color Restoration Using U-Net Denoising Autoencoder","date":"2019-05-22","arxiv_id":"1905.09000","n_code_links":0,"syntology":null},{"paper":"/paper/wpu-netboundary-learning-by-using-weighted","slug":"wpu-netboundary-learning-by-using-weighted","title":"WPU-Net: Boundary Learning by Using Weighted Propagation in Convolution Network","date":"2019-05-22","arxiv_id":"1905.09226","n_code_links":2,"syntology":null},{"paper":null,"slug":"convolutions-on-spherical-images","title":"Convolutions on Spherical Images","date":"2019-05-21","arxiv_id":"1905.08409","n_code_links":0,"syntology":null},{"paper":null,"slug":"dual-branch-residual-network-for-lung-nodule","title":"Dual-branch residual network for lung nodule segmentation","date":"2019-05-21","arxiv_id":"1905.08413","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-bias-in-gan-based-data-augmentation","title":"Exploring Bias in GAN-based Data Augmentation for Small Samples","date":"2019-05-21","arxiv_id":"1905.08495","n_code_links":0,"syntology":null},{"paper":"/paper/parallel-neural-text-to-speech","slug":"parallel-neural-text-to-speech","title":"Non-Autoregressive Neural Text-to-Speech","date":"2019-05-21","arxiv_id":"1905.08459","n_code_links":2,"syntology":{"ran":6,"of":7,"n_ran_checked":4,"n_instrument":2,"unverified":1,"pointer_only":0,"phrase":"6 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; 2 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"s-flow-gan","title":"S-Flow GAN","date":"2019-05-21","arxiv_id":"1905.08474","n_code_links":0,"syntology":null},{"paper":null,"slug":"task-decomposition-and-synchronization-for","title":"Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation","date":"2019-05-21","arxiv_id":"1905.08720","n_code_links":0,"syntology":null},{"paper":null,"slug":"darc-differentiable-architecture-compression","title":"DARC: Differentiable ARchitecture Compression","date":"2019-05-20","arxiv_id":"1905.08170","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-transformer-with-multi-view-visual","title":"Multimodal Transformer with Multi-View Visual Representation for Image Captioning","date":"2019-05-20","arxiv_id":"1905.07841","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-2d-dilated-residual-u-net-for-multi-organ","title":"A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT","date":"2019-05-19","arxiv_id":"1905.07710","n_code_links":0,"syntology":null},{"paper":"/paper/forecast-clstm-a-new-convolutional-lstm","slug":"forecast-clstm-a-new-convolutional-lstm","title":"FORECAST-CLSTM: A New Convolutional LSTM Network for Cloudage Nowcasting","date":"2019-05-19","arxiv_id":"1905.07700","n_code_links":1,"syntology":null},{"paper":"/paper/geometric-pose-affordance-3d-human-pose-with","slug":"geometric-pose-affordance-3d-human-pose-with","title":"Geometric Pose Affordance: 3D Human Pose with Scene Constraints","date":"2019-05-19","arxiv_id":"1905.07718","n_code_links":0,"syntology":null},{"paper":null,"slug":"mean-field-langevin-dynamics-and-energy","title":"Mean-Field Langevin Dynamics and Energy Landscape of Neural Networks","date":"2019-05-19","arxiv_id":"1905.07769","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatio-temporal-adversarial-learning-for","title":"Spatio-Temporal Adversarial Learning for Detecting Unseen Falls","date":"2019-05-19","arxiv_id":"1905.07817","n_code_links":0,"syntology":null},{"paper":null,"slug":"u-net-based-multi-instance-video-object","title":"U-Net Based Multi-instance Video Object Segmentation","date":"2019-05-19","arxiv_id":"1905.07826","n_code_links":0,"syntology":null},{"paper":"/paper/variational-hetero-encoder-randomized","slug":"variational-hetero-encoder-randomized","title":"Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling","date":"2019-05-18","arxiv_id":"1905.08622","n_code_links":1,"syntology":null},{"paper":"/paper/a-deep-learning-approach-to-detecting-volcano","slug":"a-deep-learning-approach-to-detecting-volcano","title":"A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets","date":"2019-05-17","arxiv_id":"1905.07286","n_code_links":1,"syntology":null},{"paper":"/paper/be-your-own-teacher-improve-the-performance","slug":"be-your-own-teacher-improve-the-performance","title":"Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation","date":"2019-05-17","arxiv_id":"1905.08094","n_code_links":1,"syntology":null},{"paper":"/paper/mastering-the-game-of-sungka-from-random-play","slug":"mastering-the-game-of-sungka-from-random-play","title":"Mastering the Game of Sungka from Random Play","date":"2019-05-17","arxiv_id":"1905.07102","n_code_links":1,"syntology":null},{"paper":null,"slug":"stochastically-dominant-distributional","title":"Stochastically Dominant Distributional Reinforcement Learning","date":"2019-05-17","arxiv_id":"1905.07318","n_code_links":0,"syntology":null},{"paper":"/paper/transfer-learning-based-detection-of-diabetic","slug":"transfer-learning-based-detection-of-diabetic","title":"Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset","date":"2019-05-17","arxiv_id":"1905.07203","n_code_links":1,"syntology":null},{"paper":"/paper/190508633","slug":"190508633","title":"Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification","date":"2019-05-16","arxiv_id":"1905.08633","n_code_links":1,"syntology":null},{"paper":"/paper/deep-compressed-sensing","slug":"deep-compressed-sensing","title":"Deep Compressed Sensing","date":"2019-05-16","arxiv_id":"1905.06723","n_code_links":1,"syntology":null},{"paper":"/paper/deep-learning-for-interference-identification","slug":"deep-learning-for-interference-identification","title":"Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection","date":"2019-05-16","arxiv_id":"1905.08054","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-reference-generation-with-multi-domain","title":"Deep Reference Generation with Multi-Domain Hierarchical Constraints for Inter Prediction","date":"2019-05-16","arxiv_id":"1905.06567","n_code_links":0,"syntology":null},{"paper":null,"slug":"gated-convolutional-neural-networks-for","title":"Gated Convolutional Neural Networks for Domain Adaptation","date":"2019-05-16","arxiv_id":"1905.06906","n_code_links":0,"syntology":null},{"paper":null,"slug":"non-parametric-priors-for-generative","title":"Non-Parametric Priors For Generative Adversarial Networks","date":"2019-05-16","arxiv_id":"1905.07061","n_code_links":0,"syntology":null},{"paper":"/paper/reshapegan-object-reshaping-by-providing-a","slug":"reshapegan-object-reshaping-by-providing-a","title":"ReshapeGAN: Object Reshaping by Providing A Single Reference Image","date":"2019-05-16","arxiv_id":"1905.06514","n_code_links":1,"syntology":null},{"paper":null,"slug":"semi-supervised-learning-based-on-generative","title":"Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach","date":"2019-05-16","arxiv_id":"1905.06484","n_code_links":0,"syntology":null},{"paper":null,"slug":"trk-cnn-transferable-ranking-cnn-for-image","title":"TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes","date":"2019-05-16","arxiv_id":"1905.06509","n_code_links":0,"syntology":null},{"paper":"/paper/x2ct-gan-reconstructing-ct-from-biplanar-x","slug":"x2ct-gan-reconstructing-ct-from-biplanar-x","title":"X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks","date":"2019-05-16","arxiv_id":"1905.06902","n_code_links":1,"syntology":null},{"paper":"/paper/3d-point-cloud-generative-adversarial-network","slug":"3d-point-cloud-generative-adversarial-network","title":"3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions","date":"2019-05-15","arxiv_id":"1905.06292","n_code_links":4,"syntology":{"ran":18,"of":25,"n_ran_checked":17,"n_instrument":1,"unverified":7,"pointer_only":2,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","official":null}},{"paper":null,"slug":"3d-semantic-scene-completion-from-a-single","title":"3D Semantic Scene Completion from a Single Depth Image using Adversarial Training","date":"2019-05-15","arxiv_id":"1905.06231","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-learning-based-approach-for-fast-and","title":"A deep-learning-based approach for fast and robust steel surface defects classification","date":"2019-05-15","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/can-graph-neural-networks-go-online-an","slug":"can-graph-neural-networks-go-online-an","title":"Can Graph Neural Networks Go \"Online\"? An Analysis of Pretraining and Inference","date":"2019-05-15","arxiv_id":"1905.06018","n_code_links":1,"syntology":null},{"paper":null,"slug":"contextualized-spatial-temporal-network-for","title":"Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction","date":"2019-05-15","arxiv_id":"1905.06335","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-neural-network-channel-execution-for","title":"Dynamic Neural Network Channel Execution for Efficient Training","date":"2019-05-15","arxiv_id":"1905.06435","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-multi-channel-speech-separation","title":"End-to-End Multi-Channel Speech Separation","date":"2019-05-15","arxiv_id":"1905.06286","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-based-single-step-quantitative","title":"Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction","date":"2019-05-15","arxiv_id":"1905.05953","n_code_links":0,"syntology":null},{"paper":"/paper/online-normalization-for-training-neural","slug":"online-normalization-for-training-neural","title":"Online Normalization for Training Neural Networks","date":"2019-05-15","arxiv_id":"1905.05894","n_code_links":1,"syntology":{"ran":9,"of":16,"n_ran_checked":9,"n_instrument":0,"unverified":7,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","official":{"repos":["cerebras/online-normalization"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/190505661","slug":"190505661","title":"Efficient Ladder-style DenseNets for Semantic Segmentation of Large Images","date":"2019-05-14","arxiv_id":"1905.05661","n_code_links":3,"syntology":null},{"paper":null,"slug":"190508608","title":"3D Dense Separated Convolution Module for Volumetric Image Analysis","date":"2019-05-14","arxiv_id":"1905.08608","n_code_links":0,"syntology":null},{"paper":null,"slug":"190508610","title":"Skin Cancer Recognition using Deep Residual Network","date":"2019-05-14","arxiv_id":"1905.08610","n_code_links":0,"syntology":null},{"paper":"/paper/american-sign-language-alphabet-recognition","slug":"american-sign-language-alphabet-recognition","title":"American Sign Language Alphabet Recognition using Deep Learning","date":"2019-05-14","arxiv_id":"1905.05487","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-improved-self-supervised-gan-via","title":"An Improved Self-supervised GAN via Adversarial Training","date":"2019-05-14","arxiv_id":"1905.05469","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-recognition-system-for-recognizing","title":"End to End Recognition System for Recognizing Offline Unconstrained Vietnamese Handwriting","date":"2019-05-14","arxiv_id":"1905.05381","n_code_links":0,"syntology":null},{"paper":null,"slug":"expression-conditional-gan-for-facial","title":"Expression Conditional GAN for Facial Expression-to-Expression Translation","date":"2019-05-14","arxiv_id":"1905.05416","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-attribute-aggregation-network-with","title":"Graph Attribute Aggregation Network with Progressive Margin Folding","date":"2019-05-14","arxiv_id":"1905.05347","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-groove-with-inverse-sequence","slug":"learning-to-groove-with-inverse-sequence","title":"Learning to Groove with Inverse Sequence Transformations","date":"2019-05-14","arxiv_id":"1905.06118","n_code_links":1,"syntology":null},{"paper":"/paper/multi-scale-dynamic-graph-convolutional","slug":"multi-scale-dynamic-graph-convolutional","title":"Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification","date":"2019-05-14","arxiv_id":"1905.06133","n_code_links":1,"syntology":null},{"paper":"/paper/zero-shot-voice-style-transfer-with-only","slug":"zero-shot-voice-style-transfer-with-only","title":"AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss","date":"2019-05-14","arxiv_id":"1905.05879","n_code_links":11,"syntology":null},{"paper":null,"slug":"190508606","title":"VGG Fine-tuning for Cooking State Recognition","date":"2019-05-13","arxiv_id":"1905.08606","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-spatio-temporal-fuzzy-neural-network","title":"A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction","date":"2019-05-13","arxiv_id":"1905.05614","n_code_links":0,"syntology":null},{"paper":"/paper/cutmix-regularization-strategy-to-train","slug":"cutmix-regularization-strategy-to-train","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","date":"2019-05-13","arxiv_id":"1905.04899","n_code_links":30,"syntology":{"ran":17,"of":24,"n_ran_checked":11,"n_instrument":6,"unverified":7,"pointer_only":5,"phrase":"17 ran (of which 6 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 0 violated, 9 with no contract checked; 6 where Syntology's instrument failed) · 7 unverified","official":{"repos":["clovaai/CutMix-PyTorch"],"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":"isbnet-instance-aware-selective-branching","title":"Dynamic Routing Networks","date":"2019-05-13","arxiv_id":"1905.04849","n_code_links":0,"syntology":null},{"paper":"/paper/metricgan-generative-adversarial-networks","slug":"metricgan-generative-adversarial-networks","title":"MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement","date":"2019-05-13","arxiv_id":"1905.04874","n_code_links":5,"syntology":null},{"paper":null,"slug":"winograd-convolution-for-dnns-beyond-linear","title":"Winograd Convolution for DNNs: Beyond linear polynomials","date":"2019-05-13","arxiv_id":"1905.05233","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-composition-gan-towards-realistic","title":"Hierarchy Composition GAN for High-fidelity Image Synthesis","date":"2019-05-12","arxiv_id":"1905.04693","n_code_links":0,"syntology":null},{"paper":null,"slug":"object-detection-in-specific-traffic-scenes","title":"Object Detection in Specific Traffic Scenes using YOLOv2","date":"2019-05-12","arxiv_id":"1905.04740","n_code_links":0,"syntology":null},{"paper":"/paper/video-instance-segmentation","slug":"video-instance-segmentation","title":"Video Instance Segmentation","date":"2019-05-12","arxiv_id":"1905.04804","n_code_links":6,"syntology":{"ran":9,"of":11,"n_ran_checked":8,"n_instrument":1,"unverified":2,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["Epiphqny/VisTR"],"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":"cyclone-intensity-estimate-with-context-aware","title":"Cyclone intensity estimate with context-aware cyclegan","date":"2019-05-11","arxiv_id":"1905.04425","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-a-new-definition-of-artificial","title":"Deep Learning: a new definition of artificial neuron with double weight","date":"2019-05-11","arxiv_id":"1905.04545","n_code_links":0,"syntology":null},{"paper":"/paper/linear-range-in-gradient-descent","slug":"linear-range-in-gradient-descent","title":"Linear Range in Gradient Descent","date":"2019-05-11","arxiv_id":"1905.04561","n_code_links":1,"syntology":null},{"paper":null,"slug":"190504307","title":"Semantic Segmentation of Seismic Images","date":"2019-05-10","arxiv_id":"1905.04307","n_code_links":0,"syntology":null},{"paper":"/paper/ink-removal-from-histopathology-whole-slide","slug":"ink-removal-from-histopathology-whole-slide","title":"Ink removal from histopathology whole slide images by combining classification, detection and image generation models","date":"2019-05-10","arxiv_id":"1905.04385","n_code_links":1,"syntology":null},{"paper":null,"slug":"mobivsr-a-visual-speech-recognition-solution","title":"MobiVSR: A Visual Speech Recognition Solution for Mobile Devices","date":"2019-05-10","arxiv_id":"1905.03968","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-scale-aggregation-r-cnn-for-2d-multi","title":"Multi-scale Aggregation R-CNN for 2D Multi-person Pose Estimation","date":"2019-05-10","arxiv_id":"1905.03912","n_code_links":0,"syntology":null},{"paper":"/paper/neuroscore-a-brain-inspired-evaluation-metric","slug":"neuroscore-a-brain-inspired-evaluation-metric","title":"Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks","date":"2019-05-10","arxiv_id":"1905.04243","n_code_links":1,"syntology":null},{"paper":"/paper/region-attention-networks-for-pose-and","slug":"region-attention-networks-for-pose-and","title":"Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition","date":"2019-05-10","arxiv_id":"1905.04075","n_code_links":1,"syntology":null},{"paper":null,"slug":"single-path-nas-device-aware-efficient","title":"Single-Path NAS: Device-Aware Efficient ConvNet Design","date":"2019-05-10","arxiv_id":"1905.04159","n_code_links":0,"syntology":null},{"paper":null,"slug":"t-net-encoder-decoder-in-encoder-decoder","title":"T-Net: Nested encoder-decoder architecture for the main vessel segmentation in coronary angiography","date":"2019-05-10","arxiv_id":"1905.04197","n_code_links":0,"syntology":null},{"paper":"/paper/which-contrast-does-matter-towards-a-deep","slug":"which-contrast-does-matter-towards-a-deep","title":"Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN","date":"2019-05-10","arxiv_id":"1905.04105","n_code_links":2,"syntology":null},{"paper":null,"slug":"190503398","title":"General Method for Prime-point Cyclic Convolution over the Real Field","date":"2019-05-09","arxiv_id":"1905.03398","n_code_links":0,"syntology":null},{"paper":null,"slug":"190503493","title":"Limits of Deepfake Detection: A Robust Estimation Viewpoint","date":"2019-05-09","arxiv_id":"1905.03493","n_code_links":0,"syntology":null},{"paper":null,"slug":"190503577","title":"Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification","date":"2019-05-09","arxiv_id":"1905.03577","n_code_links":0,"syntology":null},{"paper":"/paper/190503670","slug":"190503670","title":"S4L: Self-Supervised Semi-Supervised Learning","date":"2019-05-09","arxiv_id":"1905.03670","n_code_links":1,"syntology":{"ran":14,"of":19,"n_ran_checked":14,"n_instrument":0,"unverified":5,"pointer_only":19,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":null}}],"record_sha256":"fd3a2950620b51d4303dbba05b25be1639474fd4a1e56a57f6a1c3222edf0c1a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}