{"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/163","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":163,"pages_in_order":196,"rows_per_page":100,"rows":[16201,16300],"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/162","next":"/method/convolution/papers/164","papers":[{"paper":"/paper/190503672","slug":"190503672","title":"Seesaw-Net: Convolution Neural Network With Uneven Group Convolution","date":"2019-05-09","arxiv_id":"1905.03672","n_code_links":3,"syntology":null},{"paper":"/paper/190503678","slug":"190503678","title":"What Do Single-view 3D Reconstruction Networks Learn?","date":"2019-05-09","arxiv_id":"1905.03678","n_code_links":0,"syntology":null},{"paper":null,"slug":"190503679","title":"Adversarial Defense Framework for Graph Neural Network","date":"2019-05-09","arxiv_id":"1905.03679","n_code_links":0,"syntology":null},{"paper":null,"slug":"convolutional-neural-networks-utilizing","title":"Convolutional Neural Networks Utilizing Multifunctional Spin-Hall MTJ Neurons","date":"2019-05-09","arxiv_id":"1905.03812","n_code_links":0,"syntology":null},{"paper":"/paper/hierarchical-cross-modal-talking-face","slug":"hierarchical-cross-modal-talking-face","title":"Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss","date":"2019-05-09","arxiv_id":"1905.03820","n_code_links":1,"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":{"repos":["lelechen63/ATVGnet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/think-globally-act-locally-a-deep-neural","slug":"think-globally-act-locally-a-deep-neural","title":"Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting","date":"2019-05-09","arxiv_id":"1905.03806","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["rajatsen91/deepglo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"190503288","title":"Advancements in Image Classification using Convolutional Neural Network","date":"2019-05-08","arxiv_id":"1905.03288","n_code_links":0,"syntology":null},{"paper":null,"slug":"190503330","title":"Universal Sound Separation","date":"2019-05-08","arxiv_id":"1905.03330","n_code_links":0,"syntology":null},{"paper":null,"slug":"190503356","title":"QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field","date":"2019-05-08","arxiv_id":"1905.03356","n_code_links":0,"syntology":null},{"paper":"/paper/190503381","slug":"190503381","title":"AutoAssist: A Framework to Accelerate Training of Deep Neural Networks","date":"2019-05-08","arxiv_id":"1905.03381","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/190503646","slug":"190503646","title":"TE141K: Artistic Text Benchmark for Text Effect Transfer","date":"2019-05-08","arxiv_id":"1905.03646","n_code_links":0,"syntology":null},{"paper":null,"slug":"frame-recurrent-video-inpainting-by-robust","title":"Frame-Recurrent Video Inpainting by Robust Optical Flow Inference","date":"2019-05-08","arxiv_id":"1905.02882","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-task-human-analysis-in-still-images","title":"Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation","date":"2019-05-08","arxiv_id":"1905.03003","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-variational-embedding-for-robust","slug":"adversarial-variational-embedding-for-robust","title":"Adversarial Variational Embedding for Robust Semi-supervised Learning","date":"2019-05-07","arxiv_id":"1905.02361","n_code_links":1,"syntology":null},{"paper":"/paper/fcc-gan-a-fully-connected-and-convolutional","slug":"fcc-gan-a-fully-connected-and-convolutional","title":"FCC-GAN: A Fully Connected and Convolutional Net Architecture for GANs","date":"2019-05-07","arxiv_id":"1905.02417","n_code_links":3,"syntology":null},{"paper":null,"slug":"generalization-ability-of-region-proposal","title":"Generalization ability of region proposal networks for multispectral person detection","date":"2019-05-07","arxiv_id":"1905.02758","n_code_links":0,"syntology":null},{"paper":"/paper/lighttrack-a-generic-framework-for-online-top","slug":"lighttrack-a-generic-framework-for-online-top","title":"LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking","date":"2019-05-07","arxiv_id":"1905.02822","n_code_links":2,"syntology":{"ran":11,"of":16,"n_ran_checked":10,"n_instrument":1,"unverified":5,"pointer_only":0,"phrase":"11 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; 1 where Syntology's instrument failed) · 5 unverified","official":{"repos":["Guanghan/lighttrack"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"automated-segmentation-of-lesions-in","title":"Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets","date":"2019-05-06","arxiv_id":"1905.01902","n_code_links":0,"syntology":null},{"paper":"/paper/comprehensible-context-driven-text-game","slug":"comprehensible-context-driven-text-game","title":"Comprehensible Context-driven Text Game Playing","date":"2019-05-06","arxiv_id":"1905.02265","n_code_links":2,"syntology":null},{"paper":null,"slug":"a-review-of-object-detection-models-based-on","title":"A Review of Object Detection Models based on Convolutional Neural Network","date":"2019-05-05","arxiv_id":"1905.01614","n_code_links":0,"syntology":null},{"paper":"/paper/accurate-face-detection-for-high-performance","slug":"accurate-face-detection-for-high-performance","title":"Accurate Face Detection for High Performance","date":"2019-05-05","arxiv_id":"1905.01585","n_code_links":0,"syntology":null},{"paper":null,"slug":"embedding-structured-contour-and-location","title":"Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection","date":"2019-05-05","arxiv_id":"1905.01575","n_code_links":0,"syntology":null},{"paper":"/paper/leveraging-crowdsourced-gps-data-for-road","slug":"leveraging-crowdsourced-gps-data-for-road","title":"Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery","date":"2019-05-04","arxiv_id":"1905.01447","n_code_links":0,"syntology":null},{"paper":null,"slug":"sinreq-generalized-sinusoidal-regularization","title":"SinReQ: Generalized Sinusoidal Regularization for Low-Bitwidth Deep Quantized Training","date":"2019-05-04","arxiv_id":"1905.01416","n_code_links":0,"syntology":null},{"paper":"/paper/brain-tumor-detection-using-convolutional","slug":"brain-tumor-detection-using-convolutional","title":"Brain Tumor Detection using Convolutional Neural Network","date":"2019-05-03","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"convolution-is-outer-product","title":"Convolution, attention and structure embedding","date":"2019-05-03","arxiv_id":"1905.01289","n_code_links":0,"syntology":null},{"paper":"/paper/deep-residual-reinforcement-learning","slug":"deep-residual-reinforcement-learning","title":"Deep Residual Reinforcement Learning","date":"2019-05-03","arxiv_id":"1905.01072","n_code_links":1,"syntology":null},{"paper":null,"slug":"generative-adversarial-network-for-wireless","title":"Generative Adversarial Network for Wireless Signal Spoofing","date":"2019-05-03","arxiv_id":"1905.01008","n_code_links":0,"syntology":null},{"paper":null,"slug":"stability-and-generalization-of-graph","title":"Stability and Generalization of Graph Convolutional Neural Networks","date":"2019-05-03","arxiv_id":"1905.01004","n_code_links":0,"syntology":null},{"paper":"/paper/temporal-deformable-convolutional-encoder","slug":"temporal-deformable-convolutional-encoder","title":"Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning","date":"2019-05-03","arxiv_id":"1905.01077","n_code_links":1,"syntology":null},{"paper":null,"slug":"190503709","title":"Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks","date":"2019-05-02","arxiv_id":"1905.03709","n_code_links":0,"syntology":null},{"paper":"/paper/billion-scale-semi-supervised-learning-for","slug":"billion-scale-semi-supervised-learning-for","title":"Billion-scale semi-supervised learning for image classification","date":"2019-05-02","arxiv_id":"1905.00546","n_code_links":4,"syntology":null},{"paper":null,"slug":"directing-dnns-attention-for-facial","title":"Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping","date":"2019-05-02","arxiv_id":"1905.00593","n_code_links":0,"syntology":null},{"paper":"/paper/omni-scale-feature-learning-for-person-re","slug":"omni-scale-feature-learning-for-person-re","title":"Omni-Scale Feature Learning for Person Re-Identification","date":"2019-05-02","arxiv_id":"1905.00953","n_code_links":17,"syntology":{"ran":14,"of":26,"n_ran_checked":12,"n_instrument":2,"unverified":12,"pointer_only":3,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 2 where Syntology's instrument failed) · 12 unverified","official":{"repos":["KaiyangZhou/deep-person-reid"],"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":"quality-evaluation-of-gans-using-cross-local","title":"Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality","date":"2019-05-02","arxiv_id":"1905.00643","n_code_links":0,"syntology":null},{"paper":"/paper/singan-learning-a-generative-model-from-a","slug":"singan-learning-a-generative-model-from-a","title":"SinGAN: Learning a Generative Model from a Single Natural Image","date":"2019-05-02","arxiv_id":"1905.01164","n_code_links":47,"syntology":{"ran":5,"of":11,"n_ran_checked":3,"n_instrument":2,"unverified":6,"pointer_only":3,"phrase":"5 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; 2 where Syntology's instrument failed) · 6 unverified","official":{"repos":["tamarott/SinGAN"],"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":"toward-extremely-low-bit-and-lossless","title":"Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM","date":"2019-05-02","arxiv_id":"1905.00789","n_code_links":0,"syntology":null},{"paper":null,"slug":"3dfacegan-adversarial-nets-for-3d-face","title":"3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation","date":"2019-05-01","arxiv_id":"1905.00307","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-case-for-object-compositionality-in-deep-1","title":"A Case for Object Compositionality in Deep Generative Models of Images","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-convolutional-neural-networks","slug":"adaptive-convolutional-neural-networks","title":"Adaptive Convolutional Neural Networks","date":"2019-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"aligning-artificial-neural-networks-to-the","title":"Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-games-bringing-exploration-to-robots","title":"Beyond Games: Bringing Exploration to Robots in Real-world","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"bnn-improved-binary-network-training-1","title":"BNN+: Improved Binary Network Training","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cem-rl-combining-evolutionary-and-gradient","title":"CEM-RL: Combining evolutionary and gradient-based methods for policy search","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"coco-gan-conditional-coordinate-generative","title":"COCO-GAN: Conditional Coordinate Generative Adversarial Network","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cutting-down-training-memory-by-re-fowarding-1","title":"Cutting Down Training Memory by Re-fowarding","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"d-gan-divergent-generative-adversarial","title":"D-GAN: Divergent generative adversarial network for positive unlabeled learning and counter-examples generation","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dana-scalable-out-of-the-box-distributed-asgd","title":"DANA: Scalable Out-of-the-box Distributed ASGD Without Retuning","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-bayesian-convolutional-networks-with","title":"Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-3d-shapes-using-alt-az","title":"Deep Learning 3D Shapes Using Alt-az Anisotropic 2-Sphere Convolution","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"deli-fisher-gan-stable-and-efficient-image","title":"Deli-Fisher GAN: Stable and Efficient Image Generation With Structured Latent Generative Space","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-out-of-distribution-samples-using","title":"Detecting Out-Of-Distribution Samples Using Low-Order Deep Features Statistics","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"difference-seeking-generative-adversarial","title":"Difference-Seeking Generative Adversarial Network","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"distributional-concavity-regularization-for","title":"DISTRIBUTIONAL CONCAVITY REGULARIZATION FOR GANS","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dont-let-your-discriminator-be-fooled","title":"Don't let your Discriminator be fooled","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/fast-autoaugment","slug":"fast-autoaugment","title":"Fast AutoAugment","date":"2019-05-01","arxiv_id":"1905.00397","n_code_links":11,"syntology":{"ran":37,"of":40,"n_ran_checked":21,"n_instrument":16,"unverified":3,"pointer_only":7,"phrase":"37 ran (of which 2 constructed an object rather than computing a result; 21 with no instrument failure: 0 honoured, 0 violated, 21 with no contract checked; 16 where Syntology's instrument failed) · 3 unverified","official":{"repos":["kakaobrain/fast-autoaugment"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"generative-model-based-on-minimizing-exact","title":"Generative model based on minimizing exact empirical Wasserstein distance","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/ib-gan-disentangled-representation-learning","slug":"ib-gan-disentangled-representation-learning","title":"IB-GAN: Disentangled Representation Learning with Information Bottleneck GAN","date":"2019-05-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"inducing-cooperation-via-learning-to-reshape","title":"Inducing Cooperation via Learning to reshape rewards in semi-cooperative multi-agent reinforcement learning","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/instance-aware-image-to-image-translation","slug":"instance-aware-image-to-image-translation","title":"Instance-aware Image-to-Image Translation","date":"2019-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"k-for-the-price-of-1-parameter-efficient-1","title":"K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"language-modeling-with-graph-temporal","title":"Language Modeling with Graph Temporal Convolutional Networks","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-agents-with-prioritization-and","title":"Learning agents with prioritization and parameter noise in continuous state and action space","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-latent-semantic-representation-from","title":"Learning Latent Semantic Representation from Pre-defined Generative Model","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/learning-localized-generative-models-for-3d","slug":"learning-localized-generative-models-for-3d","title":"Learning Localized Generative Models for 3D Point Clouds via Graph Convolution","date":"2019-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-search-efficient-densenet-with","title":"Learning to Search Efficient DenseNet with Layer-wise Pruning","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"local-binary-pattern-networks-for-character","title":"Local Binary Pattern Networks for Character Recognition","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"local-stability-and-performance-of-simple-1","title":"Local Stability and Performance of Simple Gradient Penalty $\\mu$-Wasserstein GAN","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"manifoldnet-a-deep-neural-network-for","title":"MANIFOLDNET: A DEEP NEURAL NETWORK FOR MANIFOLD-VALUED DATA","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"optimistic-mirror-descent-in-saddle-point-1","title":"Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/recurrent-experience-replay-in-distributed","slug":"recurrent-experience-replay-in-distributed","title":"Recurrent Experience Replay in Distributed Reinforcement Learning","date":"2019-05-01","arxiv_id":null,"n_code_links":3,"syntology":null},{"paper":"/paper/relgan-relational-generative-adversarial","slug":"relgan-relational-generative-adversarial","title":"RelGAN: Relational Generative Adversarial Networks for Text Generation","date":"2019-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/rrpn-radar-region-proposal-network-for-object","slug":"rrpn-radar-region-proposal-network-for-object","title":"RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles","date":"2019-05-01","arxiv_id":"1905.00526","n_code_links":1,"syntology":null},{"paper":null,"slug":"task-gan-for-improved-gan-based-image","title":"Task-GAN for Improved GAN based Image Restoration","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-gan-landscape-losses-architectures-1","title":"The GAN Landscape: Losses, Architectures, Regularization, and Normalization","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"traditional-and-heavy-tailed-self-1","title":"Traditional and Heavy Tailed Self Regularization in Neural Network Models","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"understand-the-dynamics-of-gans-via-primal","title":"Understand the dynamics of GANs via Primal-Dual Optimization","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unlabeled-disentangling-of-gans-with-guided","title":"Unlabeled Disentangling of GANs with Guided Siamese Networks","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"vhegan-variational-hetero-encoder-randomized","title":"VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Shot Learning","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-imitation-with-a-minimal-adversary","title":"Visual Imitation with a Minimal Adversary","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"whitening-and-coloring-transform-for-gans","title":"Whitening and Coloring transform for GANs","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-adversarial-imagination-for-sample","title":"Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning","date":"2019-04-30","arxiv_id":"1904.13255","n_code_links":0,"syntology":null},{"paper":"/paper/object-contour-and-edge-detection-with","slug":"object-contour-and-edge-detection-with","title":"Object Contour and Edge Detection with RefineContourNet","date":"2019-04-30","arxiv_id":"1904.13353","n_code_links":2,"syntology":null},{"paper":null,"slug":"resnet-can-be-pruned-60x-introducing-network","title":"ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning","date":"2019-04-30","arxiv_id":"1905.00136","n_code_links":0,"syntology":null},{"paper":"/paper/self-supervised-sequence-to-sequence-asr","slug":"self-supervised-sequence-to-sequence-asr","title":"Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text","date":"2019-04-30","arxiv_id":"1905.01152","n_code_links":0,"syntology":null},{"paper":"/paper/190503696","slug":"190503696","title":"HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision","date":"2019-04-29","arxiv_id":"1905.03696","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"pointer_only":3,"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":["zhen-dong/hawq"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["unlocated"]}}},{"paper":null,"slug":"a-convolution-recurrent-autoencoder-for","title":"A convolution recurrent autoencoder for spatio-temporal missing data imputation","date":"2019-04-29","arxiv_id":"1904.12413","n_code_links":0,"syntology":null},{"paper":null,"slug":"convtimenet-a-pre-trained-deep-convolutional","title":"ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification","date":"2019-04-29","arxiv_id":"1904.12546","n_code_links":0,"syntology":null},{"paper":"/paper/higan-cosmic-neutral-hydrogen-with-generative","slug":"higan-cosmic-neutral-hydrogen-with-generative","title":"HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks","date":"2019-04-29","arxiv_id":"1904.12846","n_code_links":1,"syntology":null},{"paper":"/paper/learning-raw-image-denoising-with-bayer","slug":"learning-raw-image-denoising-with-bayer","title":"Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation","date":"2019-04-29","arxiv_id":"1904.12945","n_code_links":1,"syntology":null},{"paper":"/paper/unsupervised-data-augmentation-1","slug":"unsupervised-data-augmentation-1","title":"Unsupervised Data Augmentation for Consistency Training","date":"2019-04-29","arxiv_id":"1904.12848","n_code_links":20,"syntology":{"ran":30,"of":52,"n_ran_checked":22,"n_instrument":8,"unverified":22,"pointer_only":17,"phrase":"30 ran (of which 3 constructed an object rather than computing a result; 22 with no instrument failure: 0 honoured, 0 violated, 22 with no contract checked; 8 where Syntology's instrument failed) · 22 unverified","official":{"repos":["google-research/uda"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":14,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/legr-filter-pruning-via-learned-global","slug":"legr-filter-pruning-via-learned-global","title":"Towards Efficient Model Compression via Learned Global Ranking","date":"2019-04-28","arxiv_id":"1904.12368","n_code_links":1,"syntology":{"ran":10,"of":13,"n_ran_checked":7,"n_instrument":3,"unverified":3,"pointer_only":1,"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) · 3 unverified","official":{"repos":["cmu-enyac/LeGR"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/rl-gan-net-a-reinforcement-learning-agent","slug":"rl-gan-net-a-reinforcement-learning-agent","title":"RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion","date":"2019-04-28","arxiv_id":"1904.12304","n_code_links":2,"syntology":{"ran":6,"of":9,"n_ran_checked":5,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"neural-source-filter-waveform-models-for","title":"Neural source-filter waveform models for statistical parametric speech synthesis","date":"2019-04-27","arxiv_id":"1904.12088","n_code_links":0,"syntology":null},{"paper":null,"slug":"segmented-convolutional-gated-recurrent","title":"Segmented convolutional gated recurrent neural networks for human activity recognition in ultra-wideband radar","date":"2019-04-27","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/actional-structural-graph-convolutional","slug":"actional-structural-graph-convolutional","title":"Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition","date":"2019-04-26","arxiv_id":"1904.12659","n_code_links":1,"syntology":{"ran":14,"of":18,"n_ran_checked":12,"n_instrument":2,"unverified":4,"pointer_only":5,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 2 honoured, 1 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["limaosen0/AS-GCN"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"automatic-traffic-sign-detection-and","title":"Automatic Traffic Sign Detection and Recognition Using SegU-Net and a Modified Tversky Loss Function With L1-Constraint","date":"2019-04-26","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/box-driven-class-wise-region-masking-and","slug":"box-driven-class-wise-region-masking-and","title":"Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation","date":"2019-04-26","arxiv_id":"1904.11693","n_code_links":1,"syntology":null},{"paper":"/paper/graph-optimized-convolutional-networks","slug":"graph-optimized-convolutional-networks","title":"Robust Graph Data Learning via Latent Graph Convolutional Representation","date":"2019-04-26","arxiv_id":"1904.11883","n_code_links":0,"syntology":null},{"paper":"/paper/on-exact-computation-with-an-infinitely-wide","slug":"on-exact-computation-with-an-infinitely-wide","title":"On Exact Computation with an Infinitely Wide Neural Net","date":"2019-04-26","arxiv_id":"1904.11955","n_code_links":2,"syntology":null}],"record_sha256":"e2647853d14c6c0505059fb7a6dac40705614d3e284288655a518a3bfe0046c9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}