{"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/23","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":23,"pages_in_order":41,"rows_per_page":100,"rows":[2201,2300],"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/22","next":"/method/global-average-pooling/papers/24","papers":[{"paper":"/paper/equivariance-bridged-so-2-invariant","slug":"equivariance-bridged-so-2-invariant","title":"Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network","date":"2021-06-18","arxiv_id":"2106.09996","n_code_links":2,"syntology":null},{"paper":"/paper/self-supervised-video-representation-learning-7","slug":"self-supervised-video-representation-learning-7","title":"Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting","date":"2021-06-18","arxiv_id":"2106.10137","n_code_links":1,"syntology":null},{"paper":null,"slug":"scaling-up-diverse-orthogonal-convolutional","title":"Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework","date":"2021-06-16","arxiv_id":"2106.09121","n_code_links":0,"syntology":null},{"paper":null,"slug":"cine-mri-detection-of-abdominal-adhesions","title":"Cine-MRI detection of abdominal adhesions with spatio-temporal deep learning","date":"2021-06-15","arxiv_id":"2106.08094","n_code_links":0,"syntology":null},{"paper":"/paper/mlp-singer-towards-rapid-parallel-singing","slug":"mlp-singer-towards-rapid-parallel-singing","title":"MLP Singer: Towards Rapid Parallel Singing Voice Synthesis","date":"2021-06-15","arxiv_id":null,"n_code_links":3,"syntology":null},{"paper":"/paper/innformant-boundary-samples-as-telltale","slug":"innformant-boundary-samples-as-telltale","title":"iNNformant: Boundary Samples as Telltale Watermarks","date":"2021-06-14","arxiv_id":"2106.07303","n_code_links":1,"syntology":null},{"paper":"/paper/latent-correlation-based-multiview-learning","slug":"latent-correlation-based-multiview-learning","title":"Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective","date":"2021-06-14","arxiv_id":"2106.07115","n_code_links":1,"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":null}},{"paper":"/paper/s-2-mlp-spatial-shift-mlp-architecture-for","slug":"s-2-mlp-spatial-shift-mlp-architecture-for","title":"S$^2$-MLP: Spatial-Shift MLP Architecture for Vision","date":"2021-06-14","arxiv_id":"2106.07477","n_code_links":1,"syntology":null},{"paper":"/paper/atlas-based-representation-and-metric","slug":"atlas-based-representation-and-metric","title":"Atlas Based Representation and Metric Learning on Manifolds","date":"2021-06-13","arxiv_id":"2106.07062","n_code_links":1,"syntology":null},{"paper":"/paper/isolated-sign-recognition-from-rgb-video","slug":"isolated-sign-recognition-from-rgb-video","title":"Isolated Sign Recognition from RGB Video using Pose Flow and Self-Attention","date":"2021-06-11","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"small-object-detection-for-near-real-time","title":"Small Object Detection for Near Real-Time Egocentric Perception in a Manual Assembly Scenario","date":"2021-06-11","arxiv_id":"2106.06403","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-low-dose-synchrotron","title":"Deep learning based low-dose synchrotron radiation CT reconstruction","date":"2021-06-09","arxiv_id":"2106.04792","n_code_links":0,"syntology":null},{"paper":null,"slug":"recurrent-inference-machines-as-inverse","title":"Recurrent Inference Machines as inverse problem solvers for MR relaxometry","date":"2021-06-08","arxiv_id":"2106.07379","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-modal-entity-alignment-in-hyperbolic","title":"Multi-modal Entity Alignment in Hyperbolic Space","date":"2021-06-07","arxiv_id":"2106.03619","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-future-is-log-gaussian-resnets-and-their","title":"The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization","date":"2021-06-07","arxiv_id":"2106.04013","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-based-type-identification-of","slug":"deep-learning-based-type-identification-of","title":"Deep Learning-based Type Identification of Volumetric MRI Sequences","date":"2021-06-06","arxiv_id":"2106.03208","n_code_links":2,"syntology":null},{"paper":null,"slug":"deep-unsupervised-learning-for-joint-antenna","title":"Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming","date":"2021-06-06","arxiv_id":"2106.03127","n_code_links":0,"syntology":null},{"paper":null,"slug":"regularization-in-resnet-with-stochastic","title":"Regularization in ResNet with Stochastic Depth","date":"2021-06-06","arxiv_id":"2106.03091","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-end-to-end-breast-tumour-classification","title":"An End-to-End Breast Tumour Classification Model Using Context-Based Patch Modelling- A BiLSTM Approach for Image Classification","date":"2021-06-05","arxiv_id":"2106.02864","n_code_links":0,"syntology":null},{"paper":null,"slug":"t-net-deep-stacked-scale-iteration-network","title":"T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing","date":"2021-06-05","arxiv_id":"2106.02809","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-learning-based-optimal-market-bidding","title":"A Learning-based Optimal Market Bidding Strategy for Price-Maker Energy Storage","date":"2021-06-04","arxiv_id":"2106.02396","n_code_links":0,"syntology":null},{"paper":null,"slug":"encoder-decoder-neural-architecture","title":"Encoder-Decoder Neural Architecture Optimization for Keyword Spotting","date":"2021-06-04","arxiv_id":"2106.02738","n_code_links":0,"syntology":null},{"paper":"/paper/ct-net-channel-tensorization-network-for-1","slug":"ct-net-channel-tensorization-network-for-1","title":"CT-Net: Channel Tensorization Network for Video Classification","date":"2021-06-03","arxiv_id":"2106.01603","n_code_links":1,"syntology":{"ran":11,"of":13,"n_ran_checked":4,"n_instrument":7,"unverified":2,"pointer_only":0,"phrase":"11 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 7 where Syntology's instrument failed) · 2 unverified","official":{"repos":["Andy1621/CT-Net"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":2,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"heart-sound-classification-considering","title":"Heart Sound Classification Considering Additive Noise and Convolutional Distortion","date":"2021-06-03","arxiv_id":"2106.01865","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-cnn-on-3d-anatomical-brain-mri","slug":"benchmarking-cnn-on-3d-anatomical-brain-mri","title":"Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learning","date":"2021-06-02","arxiv_id":"2106.01132","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-reinforcement-learning-based-uav","title":"Deep Reinforcement Learning-based UAV Navigation and Control: A Soft Actor-Critic with Hindsight Experience Replay Approach","date":"2021-06-02","arxiv_id":"2106.01016","n_code_links":0,"syntology":null},{"paper":"/paper/multiscale-domain-adaptive-yolo-for-cross","slug":"multiscale-domain-adaptive-yolo-for-cross","title":"Multiscale Domain Adaptive YOLO for Cross-Domain Object Detection","date":"2021-06-02","arxiv_id":"2106.01483","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-deeper-deep-reinforcement-learning","title":"Towards Deeper Deep Reinforcement Learning with Spectral Normalization","date":"2021-06-02","arxiv_id":"2106.01151","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-care-label-concept-a-certification-suite","title":"The Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learning","date":"2021-06-01","arxiv_id":"2106.00512","n_code_links":0,"syntology":null},{"paper":null,"slug":"hemet-a-homomorphic-encryption-friendly","title":"HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture","date":"2021-05-31","arxiv_id":"2106.00038","n_code_links":0,"syntology":null},{"paper":null,"slug":"urban-traffic-surveillance-uts-a-fully","title":"Urban Traffic Surveillance (UTS): A fully probabilistic 3D tracking approach based on 2D detections","date":"2021-05-31","arxiv_id":"2105.14993","n_code_links":0,"syntology":null},{"paper":"/paper/a-compact-and-interpretable-convolutional","slug":"a-compact-and-interpretable-convolutional","title":"A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG","date":"2021-05-30","arxiv_id":"2106.00613","n_code_links":1,"syntology":null},{"paper":"/paper/epsanet-an-efficient-pyramid-split-attention","slug":"epsanet-an-efficient-pyramid-split-attention","title":"EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network","date":"2021-05-30","arxiv_id":"2105.14447","n_code_links":4,"syntology":null},{"paper":null,"slug":"overparameterization-of-deep-resnet-zero-loss","title":"Overparameterization of deep ResNet: zero loss and mean-field analysis","date":"2021-05-30","arxiv_id":"2105.14417","n_code_links":0,"syntology":null},{"paper":null,"slug":"audio-visual-scene-classification-analysis-of","title":"Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions","date":"2021-05-28","arxiv_id":"2105.13675","n_code_links":0,"syntology":null},{"paper":"/paper/classification-of-brain-tumours-in-mr-images","slug":"classification-of-brain-tumours-in-mr-images","title":"Classification of Brain Tumours in MR Images using Deep Spatiospatial Models","date":"2021-05-28","arxiv_id":"2105.14071","n_code_links":1,"syntology":null},{"paper":"/paper/mixergan-an-mlp-based-architecture-for","slug":"mixergan-an-mlp-based-architecture-for","title":"MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation","date":"2021-05-28","arxiv_id":"2105.14110","n_code_links":1,"syntology":null},{"paper":null,"slug":"bsnn-towards-faster-and-better-conversion-of","title":"BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neurons","date":"2021-05-27","arxiv_id":"2105.12917","n_code_links":0,"syntology":null},{"paper":null,"slug":"dtnn-energy-efficient-inference-with-dendrite","title":"DTNN: Energy-efficient Inference with Dendrite Tree Inspired Neural Networks for Edge Vision Applications","date":"2021-05-25","arxiv_id":"2105.11848","n_code_links":0,"syntology":null},{"paper":"/paper/fast-federated-learning-by-balancing","slug":"fast-federated-learning-by-balancing","title":"Fast Federated Learning by Balancing Communication Trade-Offs","date":"2021-05-23","arxiv_id":"2105.11028","n_code_links":2,"syntology":null},{"paper":null,"slug":"a-novel-3d-unet-deep-learning-framework-based","title":"A Novel 3D-UNet Deep Learning Framework Based on High-Dimensional Bilateral Grid for Edge Consistent Single Image Depth Estimation","date":"2021-05-21","arxiv_id":"2105.10129","n_code_links":0,"syntology":null},{"paper":null,"slug":"distributional-generalization-characterizing","title":"Distributional Generalization: Characterizing Classifiers Beyond Test Error","date":"2021-05-21","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"agsfcos-based-on-attention-mechanism-and","title":"AGSFCOS: Based on attention mechanism and Scale-Equalizing pyramid network of object detection","date":"2021-05-20","arxiv_id":"2105.09596","n_code_links":0,"syntology":null},{"paper":"/paper/dpn-senet-a-self-attention-mechanism-neural","slug":"dpn-senet-a-self-attention-mechanism-neural","title":"DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray images","date":"2021-05-20","arxiv_id":"2105.09683","n_code_links":1,"syntology":null},{"paper":null,"slug":"pseudo-pixel-level-labeling-for-images-with","title":"Pseudo Pixel-level Labeling for Images with Evolving Content","date":"2021-05-20","arxiv_id":"2105.09975","n_code_links":0,"syntology":null},{"paper":"/paper/a-novel-lightweight-convolutional-neural","slug":"a-novel-lightweight-convolutional-neural","title":"A Novel lightweight Convolutional Neural Network, ExquisiteNetV2","date":"2021-05-19","arxiv_id":"2105.09008","n_code_links":1,"syntology":null},{"paper":"/paper/transfer-learning-approach-to-classify-the-x","slug":"transfer-learning-approach-to-classify-the-x","title":"Transfer learning approach to Classify the X-ray image that corresponds to corona disease Using ResNet50 pretrained by ChexNet","date":"2021-05-18","arxiv_id":"2105.08382","n_code_links":1,"syntology":null},{"paper":"/paper/an-effective-baseline-for-robustness-to","slug":"an-effective-baseline-for-robustness-to","title":"An Effective Baseline for Robustness to Distributional Shift","date":"2021-05-15","arxiv_id":"2105.07107","n_code_links":1,"syntology":null},{"paper":"/paper/mutualnet-adaptive-convnet-via-mutual","slug":"mutualnet-adaptive-convnet-via-mutual","title":"MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations","date":"2021-05-14","arxiv_id":"2105.07085","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":5,"n_instrument":0,"unverified":2,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["taoyang1122/MutualNet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/heunnet-extending-resnet-using-heun-s-methods","slug":"heunnet-extending-resnet-using-heun-s-methods","title":"HeunNet: Extending ResNet using Heun's Methods","date":"2021-05-13","arxiv_id":"2105.06168","n_code_links":1,"syntology":null},{"paper":null,"slug":"afinet-attentive-feature-integration-networks","title":"AFINet: Attentive Feature Integration Networks for Image Classification","date":"2021-05-10","arxiv_id":"2105.04354","n_code_links":0,"syntology":null},{"paper":null,"slug":"examining-and-mitigating-kernel-saturation-in","title":"Examining and Mitigating Kernel Saturation in Convolutional Neural Networks using Negative Images","date":"2021-05-10","arxiv_id":"2105.04128","n_code_links":0,"syntology":null},{"paper":null,"slug":"acute-lymphoblastic-leukemia-detection-from","title":"Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks","date":"2021-05-09","arxiv_id":"2105.03995","n_code_links":0,"syntology":null},{"paper":"/paper/end-to-end-optical-character-recognition-for","slug":"end-to-end-optical-character-recognition-for","title":"End-to-End Optical Character Recognition for Bengali Handwritten Words","date":"2021-05-09","arxiv_id":"2105.04020","n_code_links":1,"syntology":null},{"paper":"/paper/hyperhypernetworks-for-the-design-of-antenna","slug":"hyperhypernetworks-for-the-design-of-antenna","title":"HyperHyperNetworks for the Design of Antenna Arrays","date":"2021-05-09","arxiv_id":"2105.03838","n_code_links":1,"syntology":null},{"paper":null,"slug":"rbnn-memory-efficient-reconfigurable-deep","title":"RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Things","date":"2021-05-09","arxiv_id":"2105.03822","n_code_links":0,"syntology":null},{"paper":"/paper/active-terahertz-imaging-dataset-for","slug":"active-terahertz-imaging-dataset-for","title":"Active Terahertz Imaging Dataset for Concealed Object Detection","date":"2021-05-08","arxiv_id":"2105.03677","n_code_links":1,"syntology":null},{"paper":null,"slug":"tensor-programs-iib-architectural","title":"Tensor Programs IIb: Architectural Universality of Neural Tangent Kernel Training Dynamics","date":"2021-05-08","arxiv_id":"2105.03703","n_code_links":0,"syntology":null},{"paper":"/paper/context-based-soft-actor-critic-for","slug":"context-based-soft-actor-critic-for","title":"Context-Based Soft Actor Critic for Environments with Non-stationary Dynamics","date":"2021-05-07","arxiv_id":"2105.03310","n_code_links":1,"syntology":null},{"paper":"/paper/pareto-optimal-quantized-resnet-is-mostly-4","slug":"pareto-optimal-quantized-resnet-is-mostly-4","title":"Pareto-Optimal Quantized ResNet Is Mostly 4-bit","date":"2021-05-07","arxiv_id":"2105.03536","n_code_links":6,"syntology":null},{"paper":null,"slug":"cuab-convolutional-uncertainty-attention","title":"CUAB: Convolutional Uncertainty Attention Block Enhanced the Chest X-ray Image Analysis","date":"2021-05-05","arxiv_id":"2105.01840","n_code_links":0,"syntology":null},{"paper":"/paper/deepplastic-a-novel-approach-to-detecting","slug":"deepplastic-a-novel-approach-to-detecting","title":"A Robotic Approach towards Quantifying Epipelagic Bound Plastic Using Deep Visual Models","date":"2021-05-05","arxiv_id":"2105.01882","n_code_links":1,"syntology":null},{"paper":"/paper/soft-attention-improves-skin-cancer","slug":"soft-attention-improves-skin-cancer","title":"Soft-Attention Improves Skin Cancer Classification Performance","date":"2021-05-05","arxiv_id":"2105.03358","n_code_links":1,"syntology":null},{"paper":"/paper/mlp-mixer-an-all-mlp-architecture-for-vision","slug":"mlp-mixer-an-all-mlp-architecture-for-vision","title":"MLP-Mixer: An all-MLP Architecture for Vision","date":"2021-05-04","arxiv_id":"2105.01601","n_code_links":49,"syntology":{"ran":114,"of":134,"n_ran_checked":105,"n_instrument":9,"unverified":20,"pointer_only":40,"phrase":"114 ran (of which 77 constructed an object rather than computing a result; 105 with no instrument failure: 2 honoured, 0 violated, 103 with no contract checked; 9 where Syntology's instrument failed) · 20 unverified","official":{"repos":["google-research/vision_transformer"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","unlocated"]}}},{"paper":"/paper/ag-curesnest-a-novel-method-for-colon-polyp","slug":"ag-curesnest-a-novel-method-for-colon-polyp","title":"AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation","date":"2021-05-02","arxiv_id":"2105.00402","n_code_links":1,"syntology":null},{"paper":"/paper/an-integrated-autoencoder-based-hybrid-cnn","slug":"an-integrated-autoencoder-based-hybrid-cnn","title":"An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound","date":"2021-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"marl-multimodal-attentional-representation","title":"MARL: Multimodal Attentional Representation Learning for Disease Prediction","date":"2021-05-01","arxiv_id":"2105.00310","n_code_links":0,"syntology":null},{"paper":null,"slug":"ipatch-a-remote-adversarial-patch","title":"IPatch: A Remote Adversarial Patch","date":"2021-04-30","arxiv_id":"2105.00113","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-data-augmentation-for-object","title":"Unsupervised data augmentation for object detection","date":"2021-04-30","arxiv_id":"2104.14965","n_code_links":0,"syntology":null},{"paper":"/paper/convolutional-neural-networks-for-alzheimers","slug":"convolutional-neural-networks-for-alzheimers","title":"Convolutional neural networks for Alzheimer’s disease detection on MRI images","date":"2021-04-29","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-object-tracking-method-based-on","title":"Multi-object Tracking Method Based on Efficient Channel Attention and Switchable Atrous Convolution","date":"2021-04-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/with-a-little-help-from-my-friends-nearest","slug":"with-a-little-help-from-my-friends-nearest","title":"With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations","date":"2021-04-29","arxiv_id":"2104.14548","n_code_links":4,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 4 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) · 1 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","official":null}},{"paper":null,"slug":"filter-distribution-templates-in","title":"Filter Distribution Templates in Convolutional Networks for Image Classification Tasks","date":"2021-04-28","arxiv_id":"2104.13993","n_code_links":0,"syntology":null},{"paper":"/paper/adapting-imagenet-scale-models-to-complex","slug":"adapting-imagenet-scale-models-to-complex","title":"If your data distribution shifts, use self-learning","date":"2021-04-27","arxiv_id":"2104.12928","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["bethgelab/robustness"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/contnet-why-not-use-convolution-and","slug":"contnet-why-not-use-convolution-and","title":"ConTNet: Why not use convolution and transformer at the same time?","date":"2021-04-27","arxiv_id":"2104.13497","n_code_links":2,"syntology":null},{"paper":null,"slug":"hao-hardware-aware-neural-architecture","title":"HAO: Hardware-aware neural Architecture Optimization for Efficient Inference","date":"2021-04-26","arxiv_id":"2104.12766","n_code_links":0,"syntology":null},{"paper":"/paper/wise-srnet-a-novel-architecture-for-enhancing","slug":"wise-srnet-a-novel-architecture-for-enhancing","title":"Wise-SrNet: A Novel Architecture for Enhancing Image Classification by Learning Spatial Resolution of Feature Maps","date":"2021-04-26","arxiv_id":"2104.12294","n_code_links":2,"syntology":null},{"paper":null,"slug":"development-of-a-soft-actor-critic-deep","title":"Development of a Soft Actor Critic Deep Reinforcement Learning Approach for Harnessing Energy Flexibility in a Large Office Building","date":"2021-04-25","arxiv_id":"2104.12125","n_code_links":0,"syntology":null},{"paper":null,"slug":"revisiting-the-double-well-problem-by-deep","title":"Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network","date":"2021-04-25","arxiv_id":"2104.14657","n_code_links":0,"syntology":null},{"paper":"/paper/transformer-meets-dcfam-a-novel-semantic","slug":"transformer-meets-dcfam-a-novel-semantic","title":"A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images","date":"2021-04-25","arxiv_id":"2104.12137","n_code_links":1,"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: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["WangLibo1995/GeoSeg"],"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":null,"slug":"deepmix-online-auto-data-augmentation-for","title":"DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking","date":"2021-04-23","arxiv_id":"2104.11585","n_code_links":0,"syntology":null},{"paper":"/paper/learning-from-ambiguous-labels-for-lung","slug":"learning-from-ambiguous-labels-for-lung","title":"Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction","date":"2021-04-23","arxiv_id":"2104.11436","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["Merrical/DAR"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"meta-learning-for-skin-cancer-detection-using","title":"Meta-learning for skin cancer detection using Deep Learning Techniques","date":"2021-04-21","arxiv_id":"2104.10775","n_code_links":0,"syntology":null},{"paper":"/paper/pp-yolov2-a-practical-object-detector","slug":"pp-yolov2-a-practical-object-detector","title":"PP-YOLOv2: A Practical Object Detector","date":"2021-04-21","arxiv_id":"2104.10419","n_code_links":1,"syntology":null},{"paper":null,"slug":"rethinking-annotation-granularity-for","title":"Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study","date":"2021-04-21","arxiv_id":"2104.10553","n_code_links":0,"syntology":null},{"paper":"/paper/improving-state-of-the-art-in-detecting","slug":"improving-state-of-the-art-in-detecting","title":"Improving state-of-the-art in Detecting Student Engagement with Resnet and TCN Hybrid Network","date":"2021-04-20","arxiv_id":"2104.10122","n_code_links":1,"syntology":null},{"paper":"/paper/revisiting-the-evaluation-of-class-activation","slug":"revisiting-the-evaluation-of-class-activation","title":"Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis","date":"2021-04-20","arxiv_id":"2104.10252","n_code_links":1,"syntology":null},{"paper":null,"slug":"memory-efficient-3d-u-net-with-reversible","title":"Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation","date":"2021-04-19","arxiv_id":"2104.09648","n_code_links":0,"syntology":null},{"paper":"/paper/writing-in-the-air-unconstrained-text","slug":"writing-in-the-air-unconstrained-text","title":"Writing in The Air: Unconstrained Text Recognition from Finger Movement Using Spatio-Temporal Convolution","date":"2021-04-19","arxiv_id":"2104.09021","n_code_links":1,"syntology":null},{"paper":"/paper/dw-gan-a-discrete-wavelet-transform-gan-for","slug":"dw-gan-a-discrete-wavelet-transform-gan-for","title":"DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing","date":"2021-04-18","arxiv_id":"2104.08911","n_code_links":1,"syntology":null},{"paper":null,"slug":"resatom-system-protein-and-ligand-affinity","title":"ResAtom System: Protein and Ligand Affinity Prediction Model Based on Deep Learning","date":"2021-04-17","arxiv_id":"2105.05125","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-efficient-convolutional-network","title":"Towards Efficient Convolutional Network Models with Filter Distribution Templates","date":"2021-04-17","arxiv_id":"2104.08446","n_code_links":0,"syntology":null},{"paper":null,"slug":"estimation-of-atrial-fibrillation-from-lead-i","title":"Estimation of atrial fibrillation from lead-I ECGs: Comparison with cardiologists and machine learning model (CurAlive), a clinical validation study","date":"2021-04-15","arxiv_id":"2104.07427","n_code_links":0,"syntology":null},{"paper":null,"slug":"vision-transformer-using-low-level-chest-x","title":"Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification","date":"2021-04-15","arxiv_id":"2104.07235","n_code_links":0,"syntology":null},{"paper":"/paper/an-introduction-of-mini-alphastar","slug":"an-introduction-of-mini-alphastar","title":"An Introduction of mini-AlphaStar","date":"2021-04-14","arxiv_id":"2104.06890","n_code_links":1,"syntology":null},{"paper":"/paper/lite-hrnet-a-lightweight-high-resolution","slug":"lite-hrnet-a-lightweight-high-resolution","title":"Lite-HRNet: A Lightweight High-Resolution Network","date":"2021-04-13","arxiv_id":"2104.06403","n_code_links":17,"syntology":{"ran":13,"of":29,"n_ran_checked":11,"n_instrument":2,"unverified":16,"pointer_only":13,"phrase":"13 ran (of which 2 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 16 unverified","official":{"repos":["HRNet/Lite-HRNet"],"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-recipe-for-global-convergence-guarantee-in","title":"A Recipe for Global Convergence Guarantee in Deep Neural Networks","date":"2021-04-12","arxiv_id":"2104.05785","n_code_links":0,"syntology":null},{"paper":"/paper/covid-19-detection-using-chest-x-rays-is-lung","slug":"covid-19-detection-using-chest-x-rays-is-lung","title":"COVID-19 detection using chest X-rays: is lung segmentation important for generalization?","date":"2021-04-12","arxiv_id":"2104.06176","n_code_links":1,"syntology":null},{"paper":null,"slug":"enos-energy-aware-network-operator-search-for","title":"ENOS: Energy-Aware Network Operator Search for Hybrid Digital and Compute-in-Memory DNN Accelerators","date":"2021-04-12","arxiv_id":"2104.05217","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-covid-19-and-community-acquired","slug":"detecting-covid-19-and-community-acquired","title":"Detecting COVID-19 and Community Acquired Pneumonia using Chest CT scan images with Deep Learning","date":"2021-04-11","arxiv_id":"2104.05121","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["shubhamchaudhary2015/ct_covid19_cap_cnn"],"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","unlocated"]}}}],"record_sha256":"1acb40c6a9e51b304f1726000e7b581c4276dd7614789b1229e8a53407641e3f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}