{"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/1x1-convolution/papers/45","list_of":"/method/1x1-convolution","method":"1x1 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":45,"pages_in_order":57,"rows_per_page":100,"rows":[4401,4500],"of":5640,"counts":{"archive_papers_tagged":5640,"with_a_code_link":2516,"where_syntology_ran_a_sample":651,"not_listed_spam_title":0,"listed":5640,"listed_where_code_ran":651,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":545,"every_run_a_failure_of_syntologys_instrument":106,"listed_with_a_run_with_no_instrument_failure":545,"listed_every_run_a_failure_of_syntologys_instrument":106,"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/1x1-convolution","prev":"/method/1x1-convolution/papers/44","next":"/method/1x1-convolution/papers/46","papers":[{"paper":"/paper/pretraining-boosts-out-of-domain-robustness","slug":"pretraining-boosts-out-of-domain-robustness","title":"Pretraining boosts out-of-domain robustness for pose estimation","date":"2019-09-24","arxiv_id":"1909.11229","n_code_links":1,"syntology":null},{"paper":"/paper/subsampling-generative-adversarial-networks","slug":"subsampling-generative-adversarial-networks","title":"Subsampling Generative Adversarial Networks: Density Ratio Estimation in Feature Space with Softplus Loss","date":"2019-09-24","arxiv_id":"1909.10670","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":["UBCDingXin/DDRE_Sampling_GANs"],"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/synthetic-dataset-generation-for-object-to","slug":"synthetic-dataset-generation-for-object-to","title":"Synthetic dataset generation for object-to-model deep learning in industrial applications","date":"2019-09-24","arxiv_id":"1909.10976","n_code_links":1,"syntology":null},{"paper":"/paper/what-s-there-in-the-dark","slug":"what-s-there-in-the-dark","title":"What's There in the Dark","date":"2019-09-24","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"190910227","title":"Deep Convolutions for In-Depth Automated Rock Typing","date":"2019-09-23","arxiv_id":"1909.10227","n_code_links":0,"syntology":null},{"paper":"/paper/a-link-recognizing-disguised-faces-via-active","slug":"a-link-recognizing-disguised-faces-via-active","title":"A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain Knowledge","date":"2019-09-23","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"190909945","title":"To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?","date":"2019-09-22","arxiv_id":"1909.09945","n_code_links":0,"syntology":null},{"paper":null,"slug":"190909756","title":"Scale MLPerf-0.6 models on Google TPU-v3 Pods","date":"2019-09-21","arxiv_id":"1909.09756","n_code_links":0,"syntology":null},{"paper":"/paper/190909725","slug":"190909725","title":"Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation","date":"2019-09-20","arxiv_id":"1909.09725","n_code_links":1,"syntology":null},{"paper":null,"slug":"road-damage-detection-acquisition-system","title":"Road Damage Detection Acquisition System based on Deep Neural Networks for Physical Asset Management","date":"2019-09-19","arxiv_id":"1909.08991","n_code_links":0,"syntology":null},{"paper":"/paper/timage-a-robust-time-series-classification","slug":"timage-a-robust-time-series-classification","title":"Timage -- A Robust Time Series Classification Pipeline","date":"2019-09-19","arxiv_id":"1909.09149","n_code_links":1,"syntology":null},{"paper":null,"slug":"transfer-learning-using-cnn-for-handwritten","title":"Transfer Learning using CNN for Handwritten Devanagari Character Recognition","date":"2019-09-19","arxiv_id":"1909.08774","n_code_links":0,"syntology":null},{"paper":"/paper/adaptis-adaptive-instance-selection-network","slug":"adaptis-adaptive-instance-selection-network","title":"AdaptIS: Adaptive Instance Selection Network","date":"2019-09-17","arxiv_id":"1909.07829","n_code_links":0,"syntology":null},{"paper":"/paper/stela-a-real-time-scene-text-detector-with","slug":"stela-a-real-time-scene-text-detector-with","title":"STELA: A Real-Time Scene Text Detector with Learned Anchor","date":"2019-09-17","arxiv_id":"1909.07549","n_code_links":1,"syntology":null},{"paper":null,"slug":"comparison-of-unet-enet-and-boxenet-for","title":"Comparison of UNet, ENet, and BoxENet for Segmentation of Mast Cells in Scans of Histological Slices","date":"2019-09-15","arxiv_id":"1909.06840","n_code_links":0,"syntology":null},{"paper":"/paper/street-crossing-aid-using-light-weight-cnns","slug":"street-crossing-aid-using-light-weight-cnns","title":"Street Crossing Aid Using Light-weight CNNs for the Visually Impaired","date":"2019-09-14","arxiv_id":"1909.09598","n_code_links":1,"syntology":null},{"paper":"/paper/brain-like-object-recognition-with-high","slug":"brain-like-object-recognition-with-high","title":"Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs","date":"2019-09-13","arxiv_id":"1909.06161","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["dicarlolab/cornet","dicarlolab/neurips2019"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"defending-against-adversarial-attacks-by-3","title":"Defending Against Adversarial Attacks by Suppressing the Largest Eigenvalue of Fisher Information Matrix","date":"2019-09-13","arxiv_id":"1909.06137","n_code_links":0,"syntology":null},{"paper":"/paper/dual-graph-convolutional-network-for-semantic","slug":"dual-graph-convolutional-network-for-semantic","title":"Dual Graph Convolutional Network for Semantic Segmentation","date":"2019-09-13","arxiv_id":"1909.06121","n_code_links":6,"syntology":null},{"paper":"/paper/spatio-spectral-networks-for-color-texture","slug":"spatio-spectral-networks-for-color-texture","title":"Spatio-spectral networks for color-texture analysis","date":"2019-09-13","arxiv_id":"1909.06446","n_code_links":1,"syntology":null},{"paper":"/paper/diffgrad-an-optimization-method-for","slug":"diffgrad-an-optimization-method-for","title":"diffGrad: An Optimization Method for Convolutional Neural Networks","date":"2019-09-12","arxiv_id":"1909.11015","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-25d-hand-pose-estimation-via","title":"Efficient 2.5D Hand Pose Estimation via Auxiliary Multi-Task Training for Embedded Devices","date":"2019-09-12","arxiv_id":"1909.05897","n_code_links":0,"syntology":null},{"paper":"/paper/gresnet-graph-residuals-for-reviving-deep","slug":"gresnet-graph-residuals-for-reviving-deep","title":"GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation","date":"2019-09-12","arxiv_id":"1909.05729","n_code_links":2,"syntology":null},{"paper":null,"slug":"towards-understanding-the-importance-of","title":"Towards Understanding the Importance of Shortcut Connections in Residual Networks","date":"2019-09-10","arxiv_id":"1909.04653","n_code_links":0,"syntology":null},{"paper":null,"slug":"vacl-variance-aware-cross-layer","title":"VACL: Variance-Aware Cross-Layer Regularization for Pruning Deep Residual Networks","date":"2019-09-10","arxiv_id":"1909.04485","n_code_links":0,"syntology":null},{"paper":"/paper/video-representation-learning-by-dense","slug":"video-representation-learning-by-dense","title":"Video Representation Learning by Dense Predictive Coding","date":"2019-09-10","arxiv_id":"1909.04656","n_code_links":1,"syntology":{"ran":9,"of":11,"n_ran_checked":8,"n_instrument":1,"unverified":2,"pointer_only":0,"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":["TengdaHan/DPC"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/when-single-event-upset-meets-deep-neural","slug":"when-single-event-upset-meets-deep-neural","title":"When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies","date":"2019-09-10","arxiv_id":"1909.04697","n_code_links":1,"syntology":null},{"paper":"/paper/cbnet-a-novel-composite-backbone-network","slug":"cbnet-a-novel-composite-backbone-network","title":"CBNet: A Novel Composite Backbone Network Architecture for Object Detection","date":"2019-09-09","arxiv_id":"1909.03625","n_code_links":6,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":1,"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) · 2 unverified","official":{"repos":["PKUbahuangliuhe/CBNet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/gaussian-temporal-awareness-networks-for-1","slug":"gaussian-temporal-awareness-networks-for-1","title":"Gaussian Temporal Awareness Networks for Action Localization","date":"2019-09-09","arxiv_id":"1909.03877","n_code_links":1,"syntology":null},{"paper":"/paper/lcscnet-linear-compressing-based-skip","slug":"lcscnet-linear-compressing-based-skip","title":"LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution","date":"2019-09-09","arxiv_id":"1909.03573","n_code_links":1,"syntology":null},{"paper":null,"slug":"understanding-the-effects-of-pre-training-for","title":"Understanding the Effects of Pre-Training for Object Detectors via Eigenspectrum","date":"2019-09-09","arxiv_id":"1909.04021","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-cnn-frameworks-comparison-for-malaria","title":"Deep CNN frameworks comparison for malaria diagnosis","date":"2019-09-06","arxiv_id":"1909.02829","n_code_links":0,"syntology":null},{"paper":null,"slug":"afp-net-realtime-anchor-free-polyp-detection","title":"AFP-Net: Realtime Anchor-Free Polyp Detection in Colonoscopy","date":"2019-09-05","arxiv_id":"1909.02477","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-neural-architecture-transformation","title":"Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection","date":"2019-09-05","arxiv_id":"1909.02293","n_code_links":0,"syntology":null},{"paper":"/paper/freeanchor-learning-to-match-anchors-for","slug":"freeanchor-learning-to-match-anchors-for","title":"FreeAnchor: Learning to Match Anchors for Visual Object Detection","date":"2019-09-05","arxiv_id":"1909.02466","n_code_links":4,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["zhangxiaosong18/FreeAnchor"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/machine-learning-approach-of-automatic","slug":"machine-learning-approach-of-automatic","title":"Machine learning approach of automatic identification and counting of blood cells","date":"2019-09-05","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/semantic-correlation-promoted-shape-variant-1","slug":"semantic-correlation-promoted-shape-variant-1","title":"Semantic Correlation Promoted Shape-Variant Context for Segmentation","date":"2019-09-05","arxiv_id":"1909.02651","n_code_links":1,"syntology":null},{"paper":null,"slug":"aerial-multi-object-tracking-by-detection","title":"Aerial multi-object tracking by detection using deep association networks","date":"2019-09-04","arxiv_id":"1909.01547","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-aided-tabu-search-detection-for","title":"Deep Learning-Aided Tabu Search Detection for Large MIMO Systems","date":"2019-09-04","arxiv_id":"1909.01683","n_code_links":0,"syntology":null},{"paper":"/paper/dense-extreme-inception-network-towards-a","slug":"dense-extreme-inception-network-towards-a","title":"Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection","date":"2019-09-04","arxiv_id":"1909.01955","n_code_links":4,"syntology":{"ran":17,"of":19,"n_ran_checked":16,"n_instrument":1,"unverified":2,"pointer_only":0,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["xavysp/DexiNed"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/fast-and-efficient-model-for-real-time-tiger","slug":"fast-and-efficient-model-for-real-time-tiger","title":"Fast and Efficient Model for Real-Time Tiger Detection In The Wild","date":"2019-09-03","arxiv_id":"1909.01122","n_code_links":1,"syntology":null},{"paper":"/paper/hardnet-a-low-memory-traffic-network","slug":"hardnet-a-low-memory-traffic-network","title":"HarDNet: A Low Memory Traffic Network","date":"2019-09-03","arxiv_id":"1909.00948","n_code_links":24,"syntology":null},{"paper":null,"slug":"psdnet-and-dpdnet-efficient-channel-expansion","title":"PSDNet and DPDNet: Efficient channel expansion, Depthwise-Pointwise-Depthwise Inverted Bottleneck Block","date":"2019-09-03","arxiv_id":"1909.01026","n_code_links":0,"syntology":null},{"paper":null,"slug":"hishabnet-detection-localization-and","title":"HishabNet: Detection, Localization and Calculation of Handwritten Bengali Mathematical Expressions","date":"2019-09-02","arxiv_id":"1909.00823","n_code_links":0,"syntology":null},{"paper":"/paper/training-time-friendly-network-for-real-time","slug":"training-time-friendly-network-for-real-time","title":"Training-Time-Friendly Network for Real-Time Object Detection","date":"2019-09-02","arxiv_id":"1909.00700","n_code_links":6,"syntology":{"ran":5,"of":8,"n_ran_checked":5,"n_instrument":0,"unverified":3,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["ZJULearning/ttfnet"],"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/ebpc-extended-bit-plane-compression-for-deep","slug":"ebpc-extended-bit-plane-compression-for-deep","title":"EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators","date":"2019-08-30","arxiv_id":"1908.11645","n_code_links":2,"syntology":null},{"paper":null,"slug":"learning-rich-representations-for-structured","title":"Learning Rich Representations For Structured Visual Prediction Tasks","date":"2019-08-30","arxiv_id":"1908.11820","n_code_links":0,"syntology":null},{"paper":"/paper/multi-modal-fusion-for-end-to-end-rgb-t","slug":"multi-modal-fusion-for-end-to-end-rgb-t","title":"Multi-Modal Fusion for End-to-End RGB-T Tracking","date":"2019-08-30","arxiv_id":"1908.11714","n_code_links":1,"syntology":null},{"paper":"/paper/a-global-local-emebdding-module-for-fashion","slug":"a-global-local-emebdding-module-for-fashion","title":"A Global-Local Emebdding Module for Fashion Landmark Detection","date":"2019-08-28","arxiv_id":"1908.10548","n_code_links":1,"syntology":null},{"paper":null,"slug":"approxnet-content-and-contention-aware-video","title":"ApproxNet: Content and Contention-Aware Video Analytics System for Embedded Clients","date":"2019-08-28","arxiv_id":"1909.02068","n_code_links":0,"syntology":null},{"paper":"/paper/inception-inspired-lstm-for-next-frame-video","slug":"inception-inspired-lstm-for-next-frame-video","title":"Inception-inspired LSTM for Next-frame Video Prediction","date":"2019-08-28","arxiv_id":"1909.05622","n_code_links":2,"syntology":null},{"paper":null,"slug":"mobile-video-action-recognition","title":"Mobile Video Action Recognition","date":"2019-08-27","arxiv_id":"1908.10155","n_code_links":0,"syntology":null},{"paper":"/paper/synthetic-patches-real-images-screening-for","slug":"synthetic-patches-real-images-screening-for","title":"Synthetic patches, real images: screening for centrosome aberrations in EM images of human cancer cells","date":"2019-08-27","arxiv_id":"1908.10109","n_code_links":1,"syntology":null},{"paper":"/paper/temporal-reasoning-graph-for-activity","slug":"temporal-reasoning-graph-for-activity","title":"Temporal Reasoning Graph for Activity Recognition","date":"2019-08-27","arxiv_id":"1908.09995","n_code_links":0,"syntology":null},{"paper":"/paper/confidence-regularized-self-training","slug":"confidence-regularized-self-training","title":"Confidence Regularized Self-Training","date":"2019-08-26","arxiv_id":"1908.09822","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yzou2/CRST"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/constructing-self-motivated-pyramid","slug":"constructing-self-motivated-pyramid","title":"Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach","date":"2019-08-26","arxiv_id":"1908.09547","n_code_links":1,"syntology":null},{"paper":"/paper/gated-convolutional-networks-with-hybrid","slug":"gated-convolutional-networks-with-hybrid","title":"Gated Convolutional Networks with Hybrid Connectivity for Image Classification","date":"2019-08-26","arxiv_id":"1908.09699","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":0,"n_instrument":4,"unverified":1,"pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","official":{"repos":["winycg/HCGNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"learning-disentangled-representations-via","title":"Learning Disentangled Representations via Independent Subspaces","date":"2019-08-26","arxiv_id":"1908.08989","n_code_links":0,"syntology":null},{"paper":null,"slug":"see-more-than-once-kernel-sharing-atrous","title":"See More Than Once -- Kernel-Sharing Atrous Convolution for Semantic Segmentation","date":"2019-08-26","arxiv_id":"1908.09443","n_code_links":0,"syntology":null},{"paper":null,"slug":"plexus-convolutional-neural-network-plexusnet","title":"Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis","date":"2019-08-24","arxiv_id":"1908.09067","n_code_links":0,"syntology":null},{"paper":"/paper/mish-a-self-regularized-non-monotonic-neural","slug":"mish-a-self-regularized-non-monotonic-neural","title":"Mish: A Self Regularized Non-Monotonic Activation Function","date":"2019-08-23","arxiv_id":"1908.08681","n_code_links":9,"syntology":{"ran":9,"of":12,"n_ran_checked":8,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["digantamisra98/Mish"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/shadow-removal-via-shadow-image-decomposition","slug":"shadow-removal-via-shadow-image-decomposition","title":"Shadow Removal via Shadow Image Decomposition","date":"2019-08-23","arxiv_id":"1908.08628","n_code_links":3,"syntology":{"ran":8,"of":11,"n_ran_checked":8,"n_instrument":0,"unverified":3,"pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["lmhieu612/SID"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"multi-stream-single-shot-spatial-temporal","title":"Multi-Stream Single Shot Spatial-Temporal Action Detection","date":"2019-08-22","arxiv_id":"1908.08178","n_code_links":0,"syntology":null},{"paper":"/paper/asymmetric-non-local-neural-networks-for","slug":"asymmetric-non-local-neural-networks-for","title":"Asymmetric Non-local Neural Networks for Semantic Segmentation","date":"2019-08-21","arxiv_id":"1908.07678","n_code_links":5,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["MendelXu/ANN","donnyyou/torchcv"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/instaboost-boosting-instance-segmentation-via","slug":"instaboost-boosting-instance-segmentation-via","title":"InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting","date":"2019-08-21","arxiv_id":"1908.07801","n_code_links":3,"syntology":null},{"paper":"/paper/190807625","slug":"190807625","title":"Action recognition with spatial-temporal discriminative filter banks","date":"2019-08-20","arxiv_id":"1908.07625","n_code_links":0,"syntology":null},{"paper":"/paper/190807919","slug":"190807919","title":"Deep High-Resolution Representation Learning for Visual Recognition","date":"2019-08-20","arxiv_id":"1908.07919","n_code_links":42,"syntology":{"ran":21,"of":34,"n_ran_checked":19,"n_instrument":2,"unverified":13,"pointer_only":21,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 1 violated, 18 with no contract checked; 2 where Syntology's instrument failed) · 13 unverified","official":null}},{"paper":null,"slug":"consistent-scale-normalization-for-object","title":"Instance Scale Normalization for image understanding","date":"2019-08-20","arxiv_id":"1908.07323","n_code_links":0,"syntology":null},{"paper":"/paper/deep-active-lesion-segmentation","slug":"deep-active-lesion-segmentation","title":"Deep Active Lesion Segmentation","date":"2019-08-19","arxiv_id":"1908.06933","n_code_links":1,"syntology":null},{"paper":"/paper/a-fast-and-accurate-one-stage-approach-to","slug":"a-fast-and-accurate-one-stage-approach-to","title":"A Fast and Accurate One-Stage Approach to Visual Grounding","date":"2019-08-18","arxiv_id":"1908.06354","n_code_links":2,"syntology":{"ran":16,"of":19,"n_ran_checked":16,"n_instrument":0,"unverified":3,"pointer_only":3,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 1 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["zyang-ur/onestage_grounding"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/panet-few-shot-image-semantic-segmentation","slug":"panet-few-shot-image-semantic-segmentation","title":"PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment","date":"2019-08-18","arxiv_id":"1908.06391","n_code_links":5,"syntology":null},{"paper":null,"slug":"differentiable-learning-to-group-channels","title":"Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks","date":"2019-08-16","arxiv_id":"1908.05867","n_code_links":0,"syntology":null},{"paper":"/paper/scarletnas-bridging-the-gap-between","slug":"scarletnas-bridging-the-gap-between","title":"SCARLET-NAS: Bridging the Gap between Stability and Scalability in Weight-sharing Neural Architecture Search","date":"2019-08-16","arxiv_id":"1908.06022","n_code_links":1,"syntology":null},{"paper":null,"slug":"accelerated-cnn-training-through-gradient","title":"Accelerated CNN Training Through Gradient Approximation","date":"2019-08-15","arxiv_id":"1908.05460","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-11k-classes-large-scale-object","title":"Detecting 11K Classes: Large Scale Object Detection without Fine-Grained Bounding Boxes","date":"2019-08-14","arxiv_id":"1908.05217","n_code_links":0,"syntology":null},{"paper":null,"slug":"histographs-graphs-in-histopathology","title":"Histographs: Graphs in Histopathology","date":"2019-08-14","arxiv_id":"1908.05020","n_code_links":0,"syntology":null},{"paper":null,"slug":"frame-to-frame-aggregation-of-active-regions","title":"Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation","date":"2019-08-13","arxiv_id":"1908.04501","n_code_links":0,"syntology":null},{"paper":"/paper/matrix-nets-a-new-deep-architecture-for","slug":"matrix-nets-a-new-deep-architecture-for","title":"Matrix Nets: A New Deep Architecture for Object Detection","date":"2019-08-13","arxiv_id":"1908.04646","n_code_links":2,"syntology":null},{"paper":null,"slug":"dynamic-region-division-for-adaptive-learning","title":"Dynamic Region Division for Adaptive Learning Pedestrian Counting","date":"2019-08-12","arxiv_id":"1908.03978","n_code_links":0,"syntology":null},{"paper":"/paper/explicit-shape-encoding-for-real-time","slug":"explicit-shape-encoding-for-real-time","title":"Explicit Shape Encoding for Real-Time Instance Segmentation","date":"2019-08-12","arxiv_id":"1908.04067","n_code_links":1,"syntology":null},{"paper":"/paper/lip-local-importance-based-pooling","slug":"lip-local-importance-based-pooling","title":"LIP: Local Importance-based Pooling","date":"2019-08-12","arxiv_id":"1908.04156","n_code_links":1,"syntology":null},{"paper":"/paper/hbonet-harmonious-bottleneck-on-two","slug":"hbonet-harmonious-bottleneck-on-two","title":"HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions","date":"2019-08-11","arxiv_id":"1908.03888","n_code_links":1,"syntology":null},{"paper":null,"slug":"mobilefan-transferring-deep-hidden","title":"MobileFAN: Transferring Deep Hidden Representation for Face Alignment","date":"2019-08-11","arxiv_id":"1908.03839","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesian-inference-for-large-scale-image","title":"Bayesian Inference for Large Scale Image Classification","date":"2019-08-09","arxiv_id":"1908.03491","n_code_links":0,"syntology":null},{"paper":null,"slug":"group-pruning-using-a-bounded-lp-norm-for","title":"Group Pruning using a Bounded-Lp norm for Group Gating and Regularization","date":"2019-08-09","arxiv_id":"1908.03463","n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-tuning-models-comparisons-on-garbage","title":"Fine-Tuning Models Comparisons on Garbage Classification for Recyclability","date":"2019-08-07","arxiv_id":"1908.04393","n_code_links":0,"syntology":null},{"paper":"/paper/stm-spatiotemporal-and-motion-encoding-for","slug":"stm-spatiotemporal-and-motion-encoding-for","title":"STM: SpatioTemporal and Motion Encoding for Action Recognition","date":"2019-08-07","arxiv_id":"1908.02486","n_code_links":0,"syntology":null},{"paper":null,"slug":"abnormality-detection-in-musculoskeletal","title":"Abnormality Detection in Musculoskeletal Radiographs with Convolutional Neural Networks(Ensembles) and Performance Optimization","date":"2019-08-06","arxiv_id":"1908.02170","n_code_links":0,"syntology":null},{"paper":"/paper/semi-automatic-labeling-for-deep-learning-in","slug":"semi-automatic-labeling-for-deep-learning-in","title":"Semi-Automatic Labeling for Deep Learning in Robotics","date":"2019-08-05","arxiv_id":"1908.01862","n_code_links":1,"syntology":null},{"paper":"/paper/attentive-normalization","slug":"attentive-normalization","title":"Attentive Normalization","date":"2019-08-04","arxiv_id":"1908.01259","n_code_links":2,"syntology":null},{"paper":"/paper/blood-pressure-estimation-from","slug":"blood-pressure-estimation-from","title":"Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network","date":"2019-08-04","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/moga-searching-beyond-mobilenetv3","slug":"moga-searching-beyond-mobilenetv3","title":"MoGA: Searching Beyond MobileNetV3","date":"2019-08-04","arxiv_id":"1908.01314","n_code_links":2,"syntology":null},{"paper":"/paper/abd-net-attentive-but-diverse-person-re","slug":"abd-net-attentive-but-diverse-person-re","title":"ABD-Net: Attentive but Diverse Person Re-Identification","date":"2019-08-03","arxiv_id":"1908.01114","n_code_links":4,"syntology":{"ran":8,"of":9,"n_ran_checked":8,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["TAMU-VITA/ABD-Net"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/indices-matter-learning-to-index-for-deep","slug":"indices-matter-learning-to-index-for-deep","title":"Indices Matter: Learning to Index for Deep Image Matting","date":"2019-08-02","arxiv_id":"1908.00672","n_code_links":1,"syntology":null},{"paper":"/paper/learning-lightweight-lane-detection-cnns-by","slug":"learning-lightweight-lane-detection-cnns-by","title":"Learning Lightweight Lane Detection CNNs by Self Attention Distillation","date":"2019-08-02","arxiv_id":"1908.00821","n_code_links":2,"syntology":{"ran":7,"of":8,"n_ran_checked":5,"n_instrument":2,"unverified":1,"pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["cardwing/Codes-for-Lane-Detection"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/uncertainty-quantification-in-computer-aided","slug":"uncertainty-quantification-in-computer-aided","title":"Uncertainty Quantification in Computer-Aided Diagnosis: Make Your Model say \"I don't know\" for Ambiguous Cases","date":"2019-08-02","arxiv_id":"1908.00792","n_code_links":1,"syntology":null},{"paper":null,"slug":"accelerating-cnn-training-by-sparsifying","title":"Accelerating CNN Training by Pruning Activation Gradients","date":"2019-08-01","arxiv_id":"1908.00173","n_code_links":0,"syntology":null},{"paper":null,"slug":"scarfnet-multi-scale-features-with-deeply","title":"ScarfNet: Multi-scale Features with Deeply Fused and Redistributed Semantics for Enhanced Object Detection","date":"2019-08-01","arxiv_id":"1908.00328","n_code_links":0,"syntology":null},{"paper":null,"slug":"simultaneous-iris-and-periocular-region","title":"Simultaneous Iris and Periocular Region Detection Using Coarse Annotations","date":"2019-07-31","arxiv_id":"1908.00069","n_code_links":0,"syntology":null},{"paper":"/paper/2d-and-3d-segmentation-of-uncertain-local","slug":"2d-and-3d-segmentation-of-uncertain-local","title":"2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy","date":"2019-07-30","arxiv_id":"1907.12868","n_code_links":1,"syntology":null}],"record_sha256":"d208693974f66098e9c1e488263ed6b96683d88d418ad0ae9119b115d61f702f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}