{"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/average-pooling/papers/41","list_of":"/method/average-pooling","method":"Average Pooling","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":41,"pages_in_order":52,"rows_per_page":100,"rows":[4001,4100],"of":5125,"counts":{"archive_papers_tagged":5125,"with_a_code_link":2243,"where_syntology_ran_a_sample":586,"not_listed_spam_title":0,"listed":5125,"listed_where_code_ran":586,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":489,"every_run_a_failure_of_syntologys_instrument":97,"listed_with_a_run_with_no_instrument_failure":489,"listed_every_run_a_failure_of_syntologys_instrument":97,"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/average-pooling","prev":"/method/average-pooling/papers/40","next":"/method/average-pooling/papers/42","papers":[{"paper":"/paper/unified-generative-adversarial-networks-for","slug":"unified-generative-adversarial-networks-for","title":"Unified Generative Adversarial Networks for Controllable Image-to-Image Translation","date":"2019-12-12","arxiv_id":"1912.06112","n_code_links":1,"syntology":null},{"paper":"/paper/augfpn-improving-multi-scale-feature-learning","slug":"augfpn-improving-multi-scale-feature-learning","title":"AugFPN: Improving Multi-scale Feature Learning for Object Detection","date":"2019-12-11","arxiv_id":"1912.05384","n_code_links":2,"syntology":null},{"paper":null,"slug":"fine-grained-classification-of-rowing-teams","title":"Fine-grained Classification of Rowing teams","date":"2019-12-11","arxiv_id":"1912.05393","n_code_links":0,"syntology":null},{"paper":"/paper/linear-mode-connectivity-and-the-lottery","slug":"linear-mode-connectivity-and-the-lottery","title":"Linear Mode Connectivity and the Lottery Ticket Hypothesis","date":"2019-12-11","arxiv_id":"1912.05671","n_code_links":2,"syntology":null},{"paper":"/paper/rdsnet-a-new-deep-architecture-for-reciprocal","slug":"rdsnet-a-new-deep-architecture-for-reciprocal","title":"RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation","date":"2019-12-11","arxiv_id":"1912.05070","n_code_links":1,"syntology":null},{"paper":"/paper/self-driving-car-steering-angle-prediction","slug":"self-driving-car-steering-angle-prediction","title":"Self-Driving Car Steering Angle Prediction Based on Image Recognition","date":"2019-12-11","arxiv_id":"1912.05440","n_code_links":2,"syntology":null},{"paper":"/paper/vibe-video-inference-for-human-body-pose-and","slug":"vibe-video-inference-for-human-body-pose-and","title":"VIBE: Video Inference for Human Body Pose and Shape Estimation","date":"2019-12-11","arxiv_id":"1912.05656","n_code_links":5,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["mkocabas/VIBE"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":null,"slug":"dr-gan-conditional-generative-adversarial","title":"DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images","date":"2019-12-10","arxiv_id":"1912.04670","n_code_links":0,"syntology":null},{"paper":"/paper/removable-andor-repeated-units-emerge-in","slug":"removable-andor-repeated-units-emerge-in","title":"Frivolous Units: Wider Networks Are Not Really That Wide","date":"2019-12-10","arxiv_id":"1912.04783","n_code_links":1,"syntology":null},{"paper":"/paper/solo-segmenting-objects-by-locations","slug":"solo-segmenting-objects-by-locations","title":"SOLO: Segmenting Objects by Locations","date":"2019-12-10","arxiv_id":"1912.04488","n_code_links":24,"syntology":null},{"paper":"/paper/spinenet-learning-scale-permuted-backbone-for","slug":"spinenet-learning-scale-permuted-backbone-for","title":"SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization","date":"2019-12-10","arxiv_id":"1912.05027","n_code_links":13,"syntology":null},{"paper":null,"slug":"decision-support-system-for-detection-and","title":"Decision Support System for Detection and Classification of Skin Cancer using CNN","date":"2019-12-09","arxiv_id":"1912.03798","n_code_links":0,"syntology":null},{"paper":"/paper/individual-predictions-matter-assessing-the","slug":"individual-predictions-matter-assessing-the","title":"Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging","date":"2019-12-08","arxiv_id":"1912.03606","n_code_links":1,"syntology":null},{"paper":"/paper/salite-a-light-weight-model-for-salient","slug":"salite-a-light-weight-model-for-salient","title":"SaLite : A light-weight model for salient object detection","date":"2019-12-08","arxiv_id":"1912.03641","n_code_links":1,"syntology":null},{"paper":"/paper/dynamic-convolutions-exploiting-spatial","slug":"dynamic-convolutions-exploiting-spatial","title":"Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference","date":"2019-12-06","arxiv_id":"1912.03203","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":1,"n_instrument":3,"unverified":1,"pointer_only":5,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["thomasverelst/dynconv"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/waterfall-atrous-spatial-pooling-architecture","slug":"waterfall-atrous-spatial-pooling-architecture","title":"Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation","date":"2019-12-06","arxiv_id":"1912.03183","n_code_links":1,"syntology":null},{"paper":"/paper/augmix-a-simple-data-processing-method-to","slug":"augmix-a-simple-data-processing-method-to","title":"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty","date":"2019-12-05","arxiv_id":"1912.02781","n_code_links":15,"syntology":{"ran":46,"of":51,"n_ran_checked":9,"n_instrument":37,"unverified":5,"pointer_only":25,"phrase":"46 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 4 honoured, 2 violated, 3 with no contract checked; 37 where Syntology's instrument failed) · 5 unverified","official":{"repos":["google-research/augmix"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":"/paper/bridging-the-gap-between-anchor-based-and","slug":"bridging-the-gap-between-anchor-based-and","title":"Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection","date":"2019-12-05","arxiv_id":"1912.02424","n_code_links":13,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"4 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; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["sfzhang15/ATSS"],"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/deep-ensembles-a-loss-landscape-perspective-1","slug":"deep-ensembles-a-loss-landscape-perspective-1","title":"Deep Ensembles: A Loss Landscape Perspective","date":"2019-12-05","arxiv_id":"1912.02757","n_code_links":1,"syntology":null},{"paper":null,"slug":"phonebit-efficient-gpu-accelerated-binary","title":"PhoneBit: Efficient GPU-Accelerated Binary Neural Network Inference Engine for Mobile Phones","date":"2019-12-05","arxiv_id":"1912.04050","n_code_links":0,"syntology":null},{"paper":"/paper/scratch-that-an-evolution-based-adversarial","slug":"scratch-that-an-evolution-based-adversarial","title":"Scratch that! An Evolution-based Adversarial Attack against Neural Networks","date":"2019-12-05","arxiv_id":"1912.02316","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-understanding-residual-and-dilated","title":"Towards Understanding Residual and Dilated Dense Neural Networks via Convolutional Sparse Coding","date":"2019-12-05","arxiv_id":"1912.02605","n_code_links":0,"syntology":null},{"paper":null,"slug":"divided-we-stand-a-novel-residual-group","title":"FocusNet++: Attentive Aggregated Transformations for Efficient and Accurate Medical Image Segmentation","date":"2019-12-04","arxiv_id":"1912.02079","n_code_links":0,"syntology":null},{"paper":"/paper/embedmask-embedding-coupling-for-one-stage","slug":"embedmask-embedding-coupling-for-one-stage","title":"EmbedMask: Embedding Coupling for One-stage Instance Segmentation","date":"2019-12-04","arxiv_id":"1912.01954","n_code_links":3,"syntology":null},{"paper":"/paper/self-supervised-learning-of-pretext-invariant","slug":"self-supervised-learning-of-pretext-invariant","title":"Self-Supervised Learning of Pretext-Invariant Representations","date":"2019-12-04","arxiv_id":"1912.01991","n_code_links":7,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":4,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","official":null}},{"paper":null,"slug":"make-thunderbolts-less-frightening-predicting","title":"Make Thunderbolts Less Frightening -- Predicting Extreme Weather Using Deep Learning","date":"2019-12-03","arxiv_id":"1912.01277","n_code_links":0,"syntology":null},{"paper":"/paper/yolact-better-real-time-instance-segmentation","slug":"yolact-better-real-time-instance-segmentation","title":"YOLACT++: Better Real-time Instance Segmentation","date":"2019-12-03","arxiv_id":"1912.06218","n_code_links":36,"syntology":{"ran":37,"of":43,"n_ran_checked":33,"n_instrument":4,"unverified":6,"pointer_only":5,"phrase":"37 ran (of which 0 constructed an object rather than computing a result; 33 with no instrument failure: 2 honoured, 2 violated, 29 with no contract checked; 4 where Syntology's instrument failed) · 6 unverified","official":{"repos":["dbolya/yolact"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"automated-speech-based-screening-of","title":"Automated speech-based screening of depression using deep convolutional neural networks","date":"2019-12-02","arxiv_id":"1912.01115","n_code_links":0,"syntology":null},{"paper":null,"slug":"face-detection-with-feature-pyramids-and","title":"Face Detection with Feature Pyramids and Landmarks","date":"2019-12-02","arxiv_id":"1912.00596","n_code_links":0,"syntology":null},{"paper":"/paper/mnasfpn-learning-latency-aware-pyramid","slug":"mnasfpn-learning-latency-aware-pyramid","title":"MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices","date":"2019-12-02","arxiv_id":"1912.01106","n_code_links":2,"syntology":null},{"paper":null,"slug":"skeleton-based-activity-recognition-by-fusing","title":"Skeleton based Activity Recognition by Fusing Part-wise Spatio-temporal and Attention Driven Residues","date":"2019-12-02","arxiv_id":"1912.00576","n_code_links":0,"syntology":null},{"paper":"/paper/anodev2-a-coupled-neural-ode-framework","slug":"anodev2-a-coupled-neural-ode-framework","title":"ANODEV2: A Coupled Neural ODE Framework","date":"2019-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/autoprune-automatic-network-pruning-by","slug":"autoprune-automatic-network-pruning-by","title":"AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters","date":"2019-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/objectnet-a-large-scale-bias-controlled","slug":"objectnet-a-large-scale-bias-controlled","title":"ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models","date":"2019-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/whats-hidden-in-a-randomly-weighted-neural","slug":"whats-hidden-in-a-randomly-weighted-neural","title":"What's Hidden in a Randomly Weighted Neural Network?","date":"2019-11-29","arxiv_id":"1911.13299","n_code_links":4,"syntology":{"ran":9,"of":9,"n_ran_checked":8,"n_instrument":1,"unverified":0,"pointer_only":5,"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) · 0 unverified","official":{"repos":["allenai/hidden-networks"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"data-driven-compression-of-convolutional","title":"Data-Driven Compression of Convolutional Neural Networks","date":"2019-11-28","arxiv_id":"1911.12740","n_code_links":0,"syntology":null},{"paper":null,"slug":"qkd-quantization-aware-knowledge-distillation","title":"QKD: Quantization-aware Knowledge Distillation","date":"2019-11-28","arxiv_id":"1911.12491","n_code_links":0,"syntology":null},{"paper":"/paper/crypto-oriented-neural-architecture-design","slug":"crypto-oriented-neural-architecture-design","title":"Crypto-Oriented Neural Architecture Design","date":"2019-11-27","arxiv_id":"1911.12322","n_code_links":1,"syntology":null},{"paper":"/paper/cspnet-a-new-backbone-that-can-enhance","slug":"cspnet-a-new-backbone-that-can-enhance","title":"CSPNet: A New Backbone that can Enhance Learning Capability of CNN","date":"2019-11-27","arxiv_id":"1911.11929","n_code_links":123,"syntology":null},{"paper":"/paper/ghostnet-more-features-from-cheap-operations","slug":"ghostnet-more-features-from-cheap-operations","title":"GhostNet: More Features from Cheap Operations","date":"2019-11-27","arxiv_id":"1911.11907","n_code_links":33,"syntology":{"ran":19,"of":23,"n_ran_checked":16,"n_instrument":3,"unverified":4,"pointer_only":5,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 2 honoured, 0 violated, 14 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","official":{"repos":["huawei-noah/ghostnet"],"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":"structured-multi-hashing-for-model","title":"Structured Multi-Hashing for Model Compression","date":"2019-11-25","arxiv_id":"1911.11177","n_code_links":0,"syntology":null},{"paper":"/paper/traffic-map-prediction-using-unet-based-deep","slug":"traffic-map-prediction-using-unet-based-deep","title":"Traffic map prediction using UNet based deep convolutional neural network","date":"2019-11-25","arxiv_id":"1912.05288","n_code_links":1,"syntology":null},{"paper":"/paper/reinventing-2d-convolutions-for-3d-medical","slug":"reinventing-2d-convolutions-for-3d-medical","title":"Reinventing 2D Convolutions for 3D Images","date":"2019-11-24","arxiv_id":"1911.10477","n_code_links":2,"syntology":null},{"paper":null,"slug":"compressing-representations-for-embedded-deep","title":"Compressing Representations for Embedded Deep Learning","date":"2019-11-23","arxiv_id":"1911.10321","n_code_links":0,"syntology":null},{"paper":"/paper/constrained-linear-data-feature-mapping-for","slug":"constrained-linear-data-feature-mapping-for","title":"Constrained Linear Data-feature Mapping for Image Classification","date":"2019-11-23","arxiv_id":"1911.10428","n_code_links":1,"syntology":null},{"paper":"/paper/panoptic-deeplab-a-simple-strong-and-fast","slug":"panoptic-deeplab-a-simple-strong-and-fast","title":"Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation","date":"2019-11-22","arxiv_id":"1911.10194","n_code_links":9,"syntology":{"ran":5,"of":9,"n_ran_checked":4,"n_instrument":1,"unverified":4,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["bowenc0221/panoptic-deeplab"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"shape-detection-in-2d-ultrasound-images","title":"Shape Detection In 2D Ultrasound Images","date":"2019-11-22","arxiv_id":"1911.09863","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-examples-improve-image","slug":"adversarial-examples-improve-image","title":"Adversarial Examples Improve Image Recognition","date":"2019-11-21","arxiv_id":"1911.09665","n_code_links":6,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"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":["tensorflow/tpu"],"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":"an-end-to-end-audio-classification-system","title":"An End-to-End Audio Classification System based on Raw Waveforms and Mix-Training Strategy","date":"2019-11-21","arxiv_id":"1911.09349","n_code_links":0,"syntology":null},{"paper":"/paper/fast-sparse-convnets-1","slug":"fast-sparse-convnets-1","title":"Fast Sparse ConvNets","date":"2019-11-21","arxiv_id":"1911.09723","n_code_links":5,"syntology":null},{"paper":"/paper/filter-response-normalization-layer","slug":"filter-response-normalization-layer","title":"Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks","date":"2019-11-21","arxiv_id":"1911.09737","n_code_links":16,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"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/learning-spatial-fusion-for-single-shot","slug":"learning-spatial-fusion-for-single-shot","title":"Learning Spatial Fusion for Single-Shot Object Detection","date":"2019-11-21","arxiv_id":"1911.09516","n_code_links":1,"syntology":null},{"paper":null,"slug":"msd-multi-self-distillation-learning-via","title":"MSD: Multi-Self-Distillation Learning via Multi-classifiers within Deep Neural Networks","date":"2019-11-21","arxiv_id":"1911.09418","n_code_links":0,"syntology":null},{"paper":"/paper/efficientdet-scalable-and-efficient-object","slug":"efficientdet-scalable-and-efficient-object","title":"EfficientDet: Scalable and Efficient Object Detection","date":"2019-11-20","arxiv_id":"1911.09070","n_code_links":64,"syntology":{"ran":55,"of":70,"n_ran_checked":48,"n_instrument":7,"unverified":15,"pointer_only":7,"phrase":"55 ran (of which 1 constructed an object rather than computing a result; 48 with no instrument failure: 4 honoured, 0 violated, 44 with no contract checked; 7 where Syntology's instrument failed) · 15 unverified","official":{"repos":["google/automl"],"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":null,"slug":"experimental-exploration-of-compact","title":"Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection","date":"2019-11-20","arxiv_id":"1911.09010","n_code_links":0,"syntology":null},{"paper":"/paper/real-time-scene-text-detection-with","slug":"real-time-scene-text-detection-with","title":"Real-time Scene Text Detection with Differentiable Binarization","date":"2019-11-20","arxiv_id":"1911.08947","n_code_links":15,"syntology":{"ran":20,"of":25,"n_ran_checked":17,"n_instrument":3,"unverified":5,"pointer_only":1,"phrase":"20 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; 3 where Syntology's instrument failed) · 5 unverified","official":{"repos":["MhLiao/DB"],"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":"differentiating-features-for-scene","title":"Differentiating Features for Scene Segmentation Based on Dedicated Attention Mechanisms","date":"2019-11-19","arxiv_id":"1911.08149","n_code_links":0,"syntology":null},{"paper":"/paper/end-to-end-asr-from-supervised-to-semi","slug":"end-to-end-asr-from-supervised-to-semi","title":"End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures","date":"2019-11-19","arxiv_id":"1911.08460","n_code_links":1,"syntology":null},{"paper":"/paper/ifq-net-integrated-fixed-point-quantization","slug":"ifq-net-integrated-fixed-point-quantization","title":"IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision","date":"2019-11-19","arxiv_id":"1911.08076","n_code_links":0,"syntology":null},{"paper":"/paper/kiss-keeping-it-simple-for-scene-text","slug":"kiss-keeping-it-simple-for-scene-text","title":"KISS: Keeping It Simple for Scene Text Recognition","date":"2019-11-19","arxiv_id":"1911.08400","n_code_links":1,"syntology":null},{"paper":"/paper/mastering-atari-go-chess-and-shogi-by","slug":"mastering-atari-go-chess-and-shogi-by","title":"Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model","date":"2019-11-19","arxiv_id":"1911.08265","n_code_links":18,"syntology":{"ran":43,"of":64,"n_ran_checked":43,"n_instrument":0,"unverified":21,"pointer_only":62,"phrase":"43 ran (of which 36 constructed an object rather than computing a result; 43 with no instrument failure: 4 honoured, 0 violated, 39 with no contract checked; 0 where Syntology's instrument failed) · 21 unverified","official":null}},{"paper":null,"slug":"ai-based-pilgrim-detection-using","title":"AI-based Pilgrim Detection using Convolutional Neural Networks","date":"2019-11-18","arxiv_id":"1911.07509","n_code_links":0,"syntology":null},{"paper":"/paper/directpose-direct-end-to-end-multi-person","slug":"directpose-direct-end-to-end-multi-person","title":"DirectPose: Direct End-to-End Multi-Person Pose Estimation","date":"2019-11-18","arxiv_id":"1911.07451","n_code_links":9,"syntology":null},{"paper":"/paper/segmentation-guided-attention-network-for","slug":"segmentation-guided-attention-network-for","title":"Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss","date":"2019-11-18","arxiv_id":"1911.07990","n_code_links":1,"syntology":null},{"paper":"/paper/sognet-scene-overlap-graph-network-for","slug":"sognet-scene-overlap-graph-network-for","title":"SOGNet: Scene Overlap Graph Network for Panoptic Segmentation","date":"2019-11-18","arxiv_id":"1911.07527","n_code_links":1,"syntology":null},{"paper":null,"slug":"dense-color-constancy-with-effective-edge","title":"ADCC: An Effective and Intelligent Attention Dense Color Constancy System for Studying Images in Smart Cities","date":"2019-11-17","arxiv_id":"1911.07163","n_code_links":0,"syntology":null},{"paper":"/paper/elope-fine-grained-visual-classification-with","slug":"elope-fine-grained-visual-classification-with","title":"ELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and Embedding","date":"2019-11-17","arxiv_id":"1911.07344","n_code_links":1,"syntology":null},{"paper":null,"slug":"music-theme-recognition-using-cnn-and-self","title":"Music theme recognition using CNN and self-attention","date":"2019-11-16","arxiv_id":"1911.07041","n_code_links":0,"syntology":null},{"paper":"/paper/centermask-real-time-anchor-free-instance-1","slug":"centermask-real-time-anchor-free-instance-1","title":"CenterMask : Real-Time Anchor-Free Instance Segmentation","date":"2019-11-15","arxiv_id":"1911.06667","n_code_links":8,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["youngwanLEE/CenterMask"],"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":["listed","official"]}}},{"paper":null,"slug":"cross-modal-supervised-learning-for-better","title":"Cross-modal supervised learning for better acoustic representations","date":"2019-11-15","arxiv_id":"1911.07917","n_code_links":0,"syntology":null},{"paper":null,"slug":"contrast-phase-classification-with-a","title":"Contrast Phase Classification with a Generative Adversarial Network","date":"2019-11-14","arxiv_id":"1911.06395","n_code_links":0,"syntology":null},{"paper":null,"slug":"image-based-feature-representation-for","title":"Image-Based Feature Representation for Insider Threat Classification","date":"2019-11-13","arxiv_id":"1911.05879","n_code_links":0,"syntology":null},{"paper":"/paper/location-aware-upsampling-for-semantic","slug":"location-aware-upsampling-for-semantic","title":"Location-aware Upsampling for Semantic Segmentation","date":"2019-11-13","arxiv_id":"1911.05250","n_code_links":1,"syntology":null},{"paper":"/paper/momentum-contrast-for-unsupervised-visual","slug":"momentum-contrast-for-unsupervised-visual","title":"Momentum Contrast for Unsupervised Visual Representation Learning","date":"2019-11-13","arxiv_id":"1911.05722","n_code_links":44,"syntology":{"ran":28,"of":42,"n_ran_checked":23,"n_instrument":5,"unverified":14,"pointer_only":19,"phrase":"28 ran (of which 16 constructed an object rather than computing a result; 23 with no instrument failure: 0 honoured, 0 violated, 23 with no contract checked; 5 where Syntology's instrument failed) · 14 unverified","official":{"repos":["facebookresearch/moco","ppwwyyxx/moco.tensorflow"],"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":"activity-monitoring-of-islamic-prayer-salat","title":"Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning","date":"2019-11-11","arxiv_id":"1911.04102","n_code_links":0,"syntology":null},{"paper":"/paper/self-training-with-noisy-student-improves","slug":"self-training-with-noisy-student-improves","title":"Self-training with Noisy Student improves ImageNet classification","date":"2019-11-11","arxiv_id":"1911.04252","n_code_links":13,"syntology":{"ran":13,"of":24,"n_ran_checked":10,"n_instrument":3,"unverified":11,"pointer_only":2,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 2 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 11 unverified","official":{"repos":["google-research/noisystudent","tensorflow/tpu"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":10,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"stronger-convergence-results-for-deep","title":"Stronger Convergence Results for Deep Residual Networks: Network Width Scales Linearly with Training Data Size","date":"2019-11-11","arxiv_id":"1911.04351","n_code_links":0,"syntology":null},{"paper":"/paper/periodic-spectral-ergodicity-a-complexity","slug":"periodic-spectral-ergodicity-a-complexity","title":"Periodic Spectral Ergodicity: A Complexity Measure for Deep Neural Networks and Neural Architecture Search","date":"2019-11-10","arxiv_id":"1911.07831","n_code_links":1,"syntology":null},{"paper":"/paper/deepmask-an-algorithm-for-cloud-and-cloud","slug":"deepmask-an-algorithm-for-cloud-and-cloud","title":"DeepMask: an algorithm for cloud and cloud shadow detection in optical satellite remote sensing images using deep residual network","date":"2019-11-09","arxiv_id":"1911.03607","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficacy-of-pixel-level-ood-detection-for-1","title":"Efficacy of Pixel-Level OOD Detection for Semantic Segmentation","date":"2019-11-07","arxiv_id":"1911.02897","n_code_links":0,"syntology":null},{"paper":null,"slug":"predictive-modeling-of-brain-tumor-a-deep","title":"Predictive modeling of brain tumor: A Deep learning approach","date":"2019-11-06","arxiv_id":"1911.02265","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-scalable-multilabel-classification-to","title":"A Scalable Multilabel Classification to Deploy Deep Learning Architectures For Edge Devices","date":"2019-11-05","arxiv_id":"1911.02098","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-target-invariant-representation","slug":"adversarial-target-invariant-representation","title":"Generalizing to unseen domains via distribution matching","date":"2019-11-03","arxiv_id":"1911.00804","n_code_links":2,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":null}},{"paper":null,"slug":"enhanced-convolutional-neural-tangent-kernels-1","title":"Enhanced Convolutional Neural Tangent Kernels","date":"2019-11-03","arxiv_id":"1911.00809","n_code_links":0,"syntology":null},{"paper":null,"slug":"comb-convolution-for-efficient-convolutional","title":"Comb Convolution for Efficient Convolutional Architecture","date":"2019-11-01","arxiv_id":"1911.00387","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-video-based-apparent-personality","title":"Multimodal Video-based Apparent Personality Recognition Using Long Short-Term Memory and Convolutional Neural Networks","date":"2019-11-01","arxiv_id":"1911.00381","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-scale-octave-convolutions-for-robust","title":"Multi-scale Octave Convolutions for Robust Speech Recognition","date":"2019-10-31","arxiv_id":"1910.14443","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-interaction-between-deep-detectors-and","title":"On the Interaction Between Deep Detectors and Siamese Trackers in Video Surveillance","date":"2019-10-31","arxiv_id":"1910.14552","n_code_links":0,"syntology":null},{"paper":null,"slug":"leanconvnets-low-cost-yet-effective","title":"LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks","date":"2019-10-29","arxiv_id":"1910.13157","n_code_links":0,"syntology":null},{"paper":"/paper/skip-clip-self-supervised-spatiotemporal","slug":"skip-clip-self-supervised-spatiotemporal","title":"Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking","date":"2019-10-28","arxiv_id":"1910.12770","n_code_links":0,"syntology":null},{"paper":"/paper/an-adaptive-and-momental-bound-method-for","slug":"an-adaptive-and-momental-bound-method-for","title":"An Adaptive and Momental Bound Method for Stochastic Learning","date":"2019-10-27","arxiv_id":"1910.12249","n_code_links":2,"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: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["lancopku/AdaMod"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/consistency-regularization-for-generative-1","slug":"consistency-regularization-for-generative-1","title":"Consistency Regularization for Generative Adversarial Networks","date":"2019-10-26","arxiv_id":"1910.12027","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-and-control-algorithms-of","title":"Deep Learning and Control Algorithms of Direct Perception for Autonomous Driving","date":"2019-10-26","arxiv_id":"1910.12031","n_code_links":0,"syntology":null},{"paper":"/paper/attention-guided-lightweight-network-for-real","slug":"attention-guided-lightweight-network-for-real","title":"Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instruments","date":"2019-10-24","arxiv_id":"1910.11109","n_code_links":1,"syntology":null},{"paper":null,"slug":"emotion-recognition-with-4kresolution","title":"Emotion recognition with 4kresolution database","date":"2019-10-24","arxiv_id":"1910.11276","n_code_links":0,"syntology":null},{"paper":null,"slug":"unified-multi-scale-feature-abstraction-for","title":"Unified Multi-scale Feature Abstraction for Medical Image Segmentation","date":"2019-10-24","arxiv_id":"1910.11456","n_code_links":0,"syntology":null},{"paper":null,"slug":"identification-of-primary-angle-closure-on-as","title":"Identification of primary angle-closure on AS-OCT images with Convolutional Neural Networks","date":"2019-10-23","arxiv_id":"1910.10414","n_code_links":0,"syntology":null},{"paper":"/paper/4-connected-shift-residual-networks","slug":"4-connected-shift-residual-networks","title":"4-Connected Shift Residual Networks","date":"2019-10-22","arxiv_id":"1910.09931","n_code_links":1,"syntology":null},{"paper":null,"slug":"establishing-an-evaluation-metric-to-quantify","title":"Establishing an Evaluation Metric to Quantify Climate Change Image Realism","date":"2019-10-22","arxiv_id":"1910.10143","n_code_links":0,"syntology":null},{"paper":"/paper/self-correction-for-human-parsing","slug":"self-correction-for-human-parsing","title":"Self-Correction for Human Parsing","date":"2019-10-22","arxiv_id":"1910.09777","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["PeikeLi/Self-Correction-Human-Parsing"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}}],"record_sha256":"47efedae7c9aa3f75206f9df8b63a5345232e2e5a1ebd8e03e6c6d112e297977","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}