{"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/global-average-pooling/papers/22","list_of":"/method/global-average-pooling","method":"Global 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":22,"pages_in_order":41,"rows_per_page":100,"rows":[2101,2200],"of":4076,"counts":{"archive_papers_tagged":4076,"with_a_code_link":1827,"where_syntology_ran_a_sample":506,"not_listed_spam_title":0,"listed":4076,"listed_where_code_ran":506,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":421,"every_run_a_failure_of_syntologys_instrument":85,"listed_with_a_run_with_no_instrument_failure":421,"listed_every_run_a_failure_of_syntologys_instrument":85,"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/global-average-pooling","prev":"/method/global-average-pooling/papers/21","next":"/method/global-average-pooling/papers/23","papers":[{"paper":"/paper/raftmlp-do-mlp-based-models-dream-of-winning","slug":"raftmlp-do-mlp-based-models-dream-of-winning","title":"RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?","date":"2021-08-09","arxiv_id":"2108.04384","n_code_links":2,"syntology":null},{"paper":null,"slug":"tensor-yard-one-shot-algorithm-of-hardware","title":"Tensor Yard: One-Shot Algorithm of Hardware-Friendly Tensor-Train Decomposition for Convolutional Neural Networks","date":"2021-08-09","arxiv_id":"2108.04029","n_code_links":0,"syntology":null},{"paper":null,"slug":"impact-of-aliasing-on-generalization-in-deep","title":"Impact of Aliasing on Generalization in Deep Convolutional Networks","date":"2021-08-07","arxiv_id":"2108.03489","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-of-alphastar","slug":"rethinking-of-alphastar","title":"Rethinking of AlphaStar","date":"2021-08-07","arxiv_id":"2108.03452","n_code_links":2,"syntology":null},{"paper":null,"slug":"spatiotemporal-contrastive-learning-of-facial","title":"Spatiotemporal Contrastive Learning of Facial Expressions in Videos","date":"2021-08-06","arxiv_id":"2108.03064","n_code_links":0,"syntology":null},{"paper":null,"slug":"rotaflip-a-new-cnn-layer-for-regularization","title":"Rotaflip: A New CNN Layer for Regularization and Rotational Invariance in Medical Images","date":"2021-08-05","arxiv_id":"2108.02704","n_code_links":0,"syntology":null},{"paper":"/paper/generic-neural-architecture-search-via","slug":"generic-neural-architecture-search-via","title":"Generic Neural Architecture Search via Regression","date":"2021-08-04","arxiv_id":"2108.01899","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["leeyeehoo/GenNAS"],"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":["listed","official"]}}},{"paper":"/paper/lung-sound-classification-using-co-tuning-and","slug":"lung-sound-classification-using-co-tuning-and","title":"Lung Sound Classification Using Co-tuning and Stochastic Normalization","date":"2021-08-04","arxiv_id":"2108.01991","n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-based-waste-classifier-with-thermo-rapid","title":"AI Based Waste classifier with Thermo-Rapid Composting","date":"2021-08-03","arxiv_id":"2108.01394","n_code_links":0,"syntology":null},{"paper":null,"slug":"forward-looking-sonar-patch-matching-modern","title":"Forward-Looking Sonar Patch Matching: Modern CNNs, Ensembling, and Uncertainty","date":"2021-08-02","arxiv_id":"2108.01066","n_code_links":0,"syntology":null},{"paper":"/paper/group-fisher-pruning-for-practical-network","slug":"group-fisher-pruning-for-practical-network","title":"Group Fisher Pruning for Practical Network Compression","date":"2021-08-02","arxiv_id":"2108.00708","n_code_links":2,"syntology":null},{"paper":"/paper/pre-trained-models-for-sonar-images","slug":"pre-trained-models-for-sonar-images","title":"Pre-trained Models for Sonar Images","date":"2021-08-02","arxiv_id":"2108.01111","n_code_links":1,"syntology":null},{"paper":"/paper/s-2-mlpv2-improved-spatial-shift-mlp","slug":"s-2-mlpv2-improved-spatial-shift-mlp","title":"S$^2$-MLPv2: Improved Spatial-Shift MLP Architecture for Vision","date":"2021-08-02","arxiv_id":"2108.01072","n_code_links":3,"syntology":{"ran":4,"of":4,"n_ran_checked":0,"n_instrument":4,"unverified":0,"pointer_only":2,"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) · 0 unverified","official":null}},{"paper":"/paper/self-supervised-audiovisual-representation","slug":"self-supervised-audiovisual-representation","title":"Self-supervised Audiovisual Representation Learning for Remote Sensing Data","date":"2021-08-02","arxiv_id":"2108.00688","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-feature-learning-of-1d","slug":"self-supervised-feature-learning-of-1d","title":"Self-Supervised Feature Learning of 1D Convolutional Neural Networks with Contrastive Loss for Eating Detection Using an In-Ear Microphone","date":"2021-08-02","arxiv_id":"2108.00769","n_code_links":1,"syntology":null},{"paper":null,"slug":"semi-supervising-learning-transfer-learning","title":"Semi-Supervising Learning, Transfer Learning, and Knowledge Distillation with SimCLR","date":"2021-08-02","arxiv_id":"2108.00587","n_code_links":0,"syntology":null},{"paper":null,"slug":"alpha-at-semeval-2021-task-6-transformer","title":"Alpha at SemEval-2021 Task 6: Transformer Based Propaganda Classification","date":"2021-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/detectron2-object-detection-manipulating","slug":"detectron2-object-detection-manipulating","title":"Detectron2 Object Detection & Manipulating Images using Cartoonization","date":"2021-08-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"developing-a-compressed-object-detection","title":"Developing a Compressed Object Detection Model based on YOLOv4 for Deployment on Embedded GPU Platform of Autonomous System","date":"2021-08-01","arxiv_id":"2108.00392","n_code_links":0,"syntology":null},{"paper":"/paper/greedy-network-enlarging","slug":"greedy-network-enlarging","title":"Greedy Network Enlarging","date":"2021-07-31","arxiv_id":"2108.00177","n_code_links":1,"syntology":null},{"paper":null,"slug":"comparing-object-recognition-in-humans-and","title":"Comparing object recognition in humans and deep convolutional neural networks -- An eye tracking study","date":"2021-07-30","arxiv_id":"2108.00107","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-the-covid-19-identification-resnet","title":"Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges","date":"2021-07-30","arxiv_id":"2107.14549","n_code_links":0,"syntology":null},{"paper":"/paper/densely-connected-neural-networks-for","slug":"densely-connected-neural-networks-for","title":"Densely connected neural networks for nonlinear regression","date":"2021-07-29","arxiv_id":"2108.00864","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-human-pose-estimation-by-maximizing","title":"Efficient Human Pose Estimation by Maximizing Fusion and High-Level Spatial Attention","date":"2021-07-29","arxiv_id":"2107.13693","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-computer-vision-based-approach-for-driver","title":"A Computer Vision-Based Approach for Driver Distraction Recognition using Deep Learning and Genetic Algorithm Based Ensemble","date":"2021-07-28","arxiv_id":"2107.13355","n_code_links":0,"syntology":null},{"paper":"/paper/a-visual-domain-transfer-learning-approach","slug":"a-visual-domain-transfer-learning-approach","title":"A Visual Domain Transfer Learning Approach for Heartbeat Sound Classification","date":"2021-07-28","arxiv_id":"2107.13237","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-based-cough-detection-camera","title":"Deep learning based cough detection camera using enhanced features","date":"2021-07-28","arxiv_id":"2107.13260","n_code_links":0,"syntology":null},{"paper":null,"slug":"value-based-reinforcement-learning-for","title":"Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings","date":"2021-07-28","arxiv_id":"2107.13356","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-low-cost-neural-ode-with-depthwise","title":"A Low-Cost Neural ODE with Depthwise Separable Convolution for Edge Domain Adaptation on FPGAs","date":"2021-07-27","arxiv_id":"2107.12824","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-and-static-object-detection","title":"Dynamic and Static Object Detection Considering Fusion Regions and Point-wise Features","date":"2021-07-27","arxiv_id":"2107.12692","n_code_links":0,"syntology":null},{"paper":"/paper/parallel-detection-for-efficient-video","slug":"parallel-detection-for-efficient-video","title":"Parallel Detection for Efficient Video Analytics at the Edge","date":"2021-07-27","arxiv_id":"2107.12563","n_code_links":2,"syntology":null},{"paper":"/paper/workshop-on-autonomous-driving-at-cvpr-2021","slug":"workshop-on-autonomous-driving-at-cvpr-2021","title":"Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge","date":"2021-07-27","arxiv_id":"2108.04230","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"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) · 2 unverified","official":{"repos":["Megvii-BaseDetection/YOLOX"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/contextual-transformer-networks-for-visual","slug":"contextual-transformer-networks-for-visual","title":"Contextual Transformer Networks for Visual Recognition","date":"2021-07-26","arxiv_id":"2107.12292","n_code_links":7,"syntology":null},{"paper":"/paper/parametric-contrastive-learning","slug":"parametric-contrastive-learning","title":"Parametric Contrastive Learning","date":"2021-07-26","arxiv_id":"2107.12028","n_code_links":5,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["dvlab-research/parametric-contrastive-learning","jiequancui/Parametric-Contrastive-Learning"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"weakly-supervised-attention-model-for-rv","title":"Weakly Supervised Attention Model for RV StrainClassification from volumetric CTPA Scans","date":"2021-07-26","arxiv_id":"2107.12009","n_code_links":0,"syntology":null},{"paper":"/paper/use-of-speaker-recognition-approaches-for","slug":"use-of-speaker-recognition-approaches-for","title":"Use of speaker recognition approaches for learning and evaluating embedding representations of musical instrument sounds","date":"2021-07-24","arxiv_id":"2107.11506","n_code_links":1,"syntology":null},{"paper":"/paper/bias-loss-for-mobile-neural-networks","slug":"bias-loss-for-mobile-neural-networks","title":"Bias Loss for Mobile Neural Networks","date":"2021-07-23","arxiv_id":"2107.11170","n_code_links":2,"syntology":null},{"paper":null,"slug":"compositional-models-multi-task-learning-and-1","title":"Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks","date":"2021-07-23","arxiv_id":"2107.10963","n_code_links":0,"syntology":null},{"paper":"/paper/triplet-is-all-you-need-with-random-mappings","slug":"triplet-is-all-you-need-with-random-mappings","title":"Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings","date":"2021-07-22","arxiv_id":"2107.10419","n_code_links":1,"syntology":null},{"paper":"/paper/cyclemlp-a-mlp-like-architecture-for-dense","slug":"cyclemlp-a-mlp-like-architecture-for-dense","title":"CycleMLP: A MLP-like Architecture for Dense Prediction","date":"2021-07-21","arxiv_id":"2107.10224","n_code_links":8,"syntology":{"ran":10,"of":15,"n_ran_checked":10,"n_instrument":0,"unverified":5,"pointer_only":2,"phrase":"10 ran (of which 6 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["ShoufaChen/CycleMLP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":null,"slug":"on-the-memorization-properties-of-contrastive","title":"On the Memorization Properties of Contrastive Learning","date":"2021-07-21","arxiv_id":"2107.10143","n_code_links":0,"syntology":null},{"paper":"/paper/channel-wise-gated-res2net-towards-robust","slug":"channel-wise-gated-res2net-towards-robust","title":"Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks","date":"2021-07-19","arxiv_id":"2107.08803","n_code_links":2,"syntology":null},{"paper":null,"slug":"a-discriminative-semantic-ranker-for-question","title":"A Discriminative Semantic Ranker for Question Retrieval","date":"2021-07-18","arxiv_id":"2107.08345","n_code_links":0,"syntology":null},{"paper":"/paper/as-mlp-an-axial-shifted-mlp-architecture-for","slug":"as-mlp-an-axial-shifted-mlp-architecture-for","title":"AS-MLP: An Axial Shifted MLP Architecture for Vision","date":"2021-07-18","arxiv_id":"2107.08391","n_code_links":2,"syntology":{"ran":10,"of":13,"n_ran_checked":9,"n_instrument":1,"unverified":3,"pointer_only":8,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 1 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["svip-lab/AS-MLP"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/yolox-exceeding-yolo-series-in-2021","slug":"yolox-exceeding-yolo-series-in-2021","title":"YOLOX: Exceeding YOLO Series in 2021","date":"2021-07-18","arxiv_id":"2107.08430","n_code_links":42,"syntology":{"ran":16,"of":23,"n_ran_checked":15,"n_instrument":1,"unverified":7,"pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","official":{"repos":["Megvii-BaseDetection/YOLOX"],"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":"a-comparison-of-deep-learning-classification","title":"A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers","date":"2021-07-16","arxiv_id":"2107.07699","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-low-latency-energy-efficient-deep","title":"Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression","date":"2021-07-16","arxiv_id":"2107.12445","n_code_links":0,"syntology":null},{"paper":"/paper/a-fuzzy-rank-based-ensemble-of-cnn-models-for","slug":"a-fuzzy-rank-based-ensemble-of-cnn-models-for","title":"A Fuzzy Rank-based Ensemble of CNN Models for Classification of Cervical Cytology","date":"2021-07-15","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"an-efficient-and-small-convolutional-neural","title":"An Efficient and Small Convolutional Neural Network for Pest Recognition -- ExquisiteNet","date":"2021-07-15","arxiv_id":"2107.07167","n_code_links":0,"syntology":null},{"paper":null,"slug":"globally-convergent-multilevel-training-of","title":"Globally Convergent Multilevel Training of Deep Residual Networks","date":"2021-07-15","arxiv_id":"2107.07572","n_code_links":0,"syntology":null},{"paper":"/paper/recurrent-parameter-generators","slug":"recurrent-parameter-generators","title":"Compact and Optimal Deep Learning with Recurrent Parameter Generators","date":"2021-07-15","arxiv_id":"2107.07110","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["samaonline/Recurrent-Parameter-Generators"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-convolutional-neural-network-approach-to-1","slug":"a-convolutional-neural-network-approach-to-1","title":"A Convolutional Neural Network Approach to the Classification of Engineering Models","date":"2021-07-14","arxiv_id":"2107.06481","n_code_links":1,"syntology":null},{"paper":"/paper/cnn-cap-effective-convolutional-neural","slug":"cnn-cap-effective-convolutional-neural","title":"CNN-Cap: Effective Convolutional Neural Network Based Capacitance Models for Full-Chip Parasitic Extraction","date":"2021-07-14","arxiv_id":"2107.06511","n_code_links":1,"syntology":null},{"paper":null,"slug":"memory-aware-fusing-and-tiling-of-neural","title":"MAFAT: Memory-Aware Fusing and Tiling of Neural Networks for Accelerated Edge Inference","date":"2021-07-14","arxiv_id":"2107.06960","n_code_links":0,"syntology":null},{"paper":"/paper/real-time-pear-fruit-detection-and-counting","slug":"real-time-pear-fruit-detection-and-counting","title":"Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT","date":"2021-07-14","arxiv_id":null,"n_code_links":3,"syntology":null},{"paper":null,"slug":"real-time-pothole-detection-using-deep","title":"Real-Time Pothole Detection Using Deep Learning","date":"2021-07-13","arxiv_id":"2107.06356","n_code_links":0,"syntology":null},{"paper":null,"slug":"structured-directional-pruning-via","title":"Structured Directional Pruning via Perturbation Orthogonal Projection","date":"2021-07-12","arxiv_id":"2107.05328","n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-layers-susceptible-to-adversarial","title":"Identifying Layers Susceptible to Adversarial Attacks","date":"2021-07-10","arxiv_id":"2107.04827","n_code_links":0,"syntology":null},{"paper":null,"slug":"training-over-parameterized-models-with-non","title":"Training Over-parameterized Models with Non-decomposable Objectives","date":"2021-07-09","arxiv_id":"2107.04641","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-gain-control-through-deep","title":"Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection","date":"2021-07-08","arxiv_id":"2107.03792","n_code_links":0,"syntology":null},{"paper":"/paper/glit-neural-architecture-search-for-global","slug":"glit-neural-architecture-search-for-global","title":"GLiT: Neural Architecture Search for Global and Local Image Transformer","date":"2021-07-07","arxiv_id":"2107.02960","n_code_links":2,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["bychen515/glit"],"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":"scopeformer-n-cnn-vit-hybrid-model-for","title":"Scopeformer: n-CNN-ViT Hybrid Model for Intracranial Hemorrhage Classification","date":"2021-07-07","arxiv_id":"2107.04575","n_code_links":0,"syntology":null},{"paper":null,"slug":"urban-tree-species-classification-using","title":"Urban Tree Species Classification Using Aerial Imagery","date":"2021-07-07","arxiv_id":"2107.03182","n_code_links":0,"syntology":null},{"paper":"/paper/integrating-circle-kernels-into-convolutional","slug":"integrating-circle-kernels-into-convolutional","title":"Integrating Large Circular Kernels into CNNs through Neural Architecture Search","date":"2021-07-06","arxiv_id":"2107.02451","n_code_links":1,"syntology":null},{"paper":null,"slug":"automated-age-related-macular-degeneration","title":"Automated age-related macular degeneration area estimation -- first results","date":"2021-07-05","arxiv_id":"2107.02211","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-efficiency-of-various-deep-transfer","title":"A Deep Transfer Learning Approach on Identifying Glitch Wave-form in Gravitational Wave Data","date":"2021-07-05","arxiv_id":"2107.01863","n_code_links":0,"syntology":null},{"paper":"/paper/tiled-squeeze-and-excite-channel-attention","slug":"tiled-squeeze-and-excite-channel-attention","title":"Tiled Squeeze-and-Excite: Channel Attention With Local Spatial Context","date":"2021-07-05","arxiv_id":"2107.02145","n_code_links":1,"syntology":null},{"paper":"/paper/volnet-estimating-human-body-part-volumes","slug":"volnet-estimating-human-body-part-volumes","title":"VolNet: Estimating Human Body Part Volumes from a Single RGB Image","date":"2021-07-05","arxiv_id":"2107.02259","n_code_links":0,"syntology":null},{"paper":"/paper/covid-vit-classification-of-covid-19-from-ct","slug":"covid-vit-classification-of-covid-19-from-ct","title":"COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models","date":"2021-07-04","arxiv_id":"2107.01682","n_code_links":1,"syntology":null},{"paper":null,"slug":"custom-deep-neural-network-for-3d-covid-chest","title":"Custom Deep Neural Network for 3D Covid Chest CT-scan Classification","date":"2021-07-03","arxiv_id":"2107.01456","n_code_links":0,"syntology":null},{"paper":"/paper/brain-over-brawn-using-a-stereo-camera-to","slug":"brain-over-brawn-using-a-stereo-camera-to","title":"Brain over Brawn: Using a Stereo Camera to Detect, Track, and Intercept a Faster UAV by Reconstructing the Intruder's Trajectory","date":"2021-07-02","arxiv_id":"2107.00962","n_code_links":1,"syntology":null},{"paper":"/paper/how-incomplete-is-contrastive-learning","slug":"how-incomplete-is-contrastive-learning","title":"Inter-intra Variant Dual Representations forSelf-supervised Video Recognition","date":"2021-07-02","arxiv_id":"2107.01194","n_code_links":1,"syntology":null},{"paper":"/paper/rapid-neural-architecture-search-by-learning-1","slug":"rapid-neural-architecture-search-by-learning-1","title":"Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets","date":"2021-07-02","arxiv_id":"2107.00860","n_code_links":1,"syntology":{"ran":12,"of":19,"n_ran_checked":11,"n_instrument":1,"unverified":7,"pointer_only":6,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","official":{"repos":["HayeonLee/MetaD2A"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["found_in_text","official","unlocated"]}}},{"paper":null,"slug":"resist-layer-wise-decomposition-of-resnets","title":"ResIST: Layer-Wise Decomposition of ResNets for Distributed Training","date":"2021-07-02","arxiv_id":"2107.00961","n_code_links":0,"syntology":null},{"paper":"/paper/simpler-faster-stronger-breaking-the-log-k","slug":"simpler-faster-stronger-breaking-the-log-k","title":"Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE","date":"2021-07-02","arxiv_id":"2107.01152","n_code_links":1,"syntology":null},{"paper":"/paper/simple-training-strategies-and-model-scaling","slug":"simple-training-strategies-and-model-scaling","title":"Simple Training Strategies and Model Scaling for Object Detection","date":"2021-06-30","arxiv_id":"2107.00057","n_code_links":1,"syntology":null},{"paper":"/paper/small-in-distribution-changes-in-3d","slug":"small-in-distribution-changes-in-3d","title":"In-distribution adversarial attacks on object recognition models using gradient-free search","date":"2021-06-30","arxiv_id":"2106.16198","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":["spandan-madan/in_distribution_adversarial_examples","in-dist-adversarials/in_distribution_adversarial_examples"],"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/an-efficient-cervical-whole-slide-image","slug":"an-efficient-cervical-whole-slide-image","title":"An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Location Deep Features","date":"2021-06-29","arxiv_id":"2106.15113","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-understanding-the-effectiveness-of","title":"Towards Understanding the Effectiveness of Attention Mechanism","date":"2021-06-29","arxiv_id":"2106.15067","n_code_links":0,"syntology":null},{"paper":null,"slug":"achieving-real-time-object-detection-on","title":"Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search","date":"2021-06-28","arxiv_id":"2106.14943","n_code_links":0,"syntology":null},{"paper":null,"slug":"benchmarking-convolutional-neural-networks","title":"Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images","date":"2021-06-28","arxiv_id":"2106.14465","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-objective-evolutionary-approach-for","title":"Multi-objective Evolutionary Approach for Efficient Kernel Size and Shape for CNN","date":"2021-06-28","arxiv_id":"2106.14776","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-xai-approach-to-deep-learning-models-in","title":"An XAI Approach to Deep Learning Models in the Detection of DCIS","date":"2021-06-27","arxiv_id":"2106.14186","n_code_links":0,"syntology":null},{"paper":null,"slug":"mitigating-severe-over-parameterization-in","title":"Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic","date":"2021-06-27","arxiv_id":"2106.14190","n_code_links":0,"syntology":null},{"paper":"/paper/an-image-classifier-can-suffice-video","slug":"an-image-classifier-can-suffice-video","title":"Can An Image Classifier Suffice For Action Recognition?","date":"2021-06-26","arxiv_id":"2106.14104","n_code_links":1,"syntology":{"ran":9,"of":12,"n_ran_checked":6,"n_instrument":3,"unverified":3,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":{"repos":["ibm/sifar-pytorch"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/brax-a-differentiable-physics-engine-for","slug":"brax-a-differentiable-physics-engine-for","title":"Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation","date":"2021-06-24","arxiv_id":"2106.13281","n_code_links":1,"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":["google/brax"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/apnn-tc-accelerating-arbitrary-precision","slug":"apnn-tc-accelerating-arbitrary-precision","title":"APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores","date":"2021-06-23","arxiv_id":"2106.12169","n_code_links":1,"syntology":null},{"paper":"/paper/classifying-textual-data-with-pre-trained","slug":"classifying-textual-data-with-pre-trained","title":"Classifying Textual Data with Pre-trained Vision Models through Transfer Learning and Data Transformations","date":"2021-06-23","arxiv_id":"2106.12479","n_code_links":1,"syntology":null},{"paper":"/paper/deep-fake-detection-survey-of-facial","slug":"deep-fake-detection-survey-of-facial","title":"Deep Fake Detection: Survey of Facial Manipulation Detection Solutions","date":"2021-06-23","arxiv_id":"2106.12605","n_code_links":1,"syntology":null},{"paper":"/paper/real-time-instance-segmentation-with","slug":"real-time-instance-segmentation-with","title":"Real-time Instance Segmentation with Discriminative Orientation Maps","date":"2021-06-23","arxiv_id":"2106.12204","n_code_links":1,"syntology":null},{"paper":null,"slug":"obstacle-detection-for-bvlos-drones","title":"Obstacle Detection for BVLOS Drones","date":"2021-06-21","arxiv_id":"2106.11098","n_code_links":0,"syntology":null},{"paper":null,"slug":"fusion-of-complex-networks-based-global-and","title":"Fusion of Complex Networks-based Global and Local Features for Texture Classification","date":"2021-06-20","arxiv_id":"2106.10701","n_code_links":0,"syntology":null},{"paper":"/paper/multirate-training-of-neural-networks","slug":"multirate-training-of-neural-networks","title":"Multirate Training of Neural Networks","date":"2021-06-20","arxiv_id":"2106.10771","n_code_links":4,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["tiffanyvlaar/multiratetrainingofnns"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/solution-for-large-scale-long-tailed","slug":"solution-for-large-scale-long-tailed","title":"Solution for Large-scale Long-tailed Recognition with Noisy Labels","date":"2021-06-20","arxiv_id":"2106.10683","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-texture-recognition-via-exploiting-cross","title":"Deep Texture Recognition via Exploiting Cross-Layer Statistical Self-Similarity","date":"2021-06-19","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/densely-connected-multi-dilated-convolutional","slug":"densely-connected-multi-dilated-convolutional","title":"Densely Connected Multi-Dilated Convolutional Networks for Dense Prediction Tasks","date":"2021-06-19","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"gaussian-context-transformer","title":"Gaussian Context Transformer","date":"2021-06-19","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/network-pruning-via-performance-maximization","slug":"network-pruning-via-performance-maximization","title":"Network Pruning via Performance Maximization","date":"2021-06-19","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-network-facial-authentication-for","title":"Neural Network Facial Authentication for Public Electric Vehicle Charging Station","date":"2021-06-19","arxiv_id":"2106.10432","n_code_links":0,"syntology":null},{"paper":"/paper/signal-processing-based-deep-learning-for","slug":"signal-processing-based-deep-learning-for","title":"Signal Processing Based Deep Learning for Blind Symbol Decoding and Modulation Classification","date":"2021-06-19","arxiv_id":"2106.10543","n_code_links":1,"syntology":null}],"record_sha256":"3536c1cd64e7c1c5cc42d970836e5f1643ad5f61e4b6cc0a52b3090027ecde6d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}