{"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/batch-normalization/papers/33","list_of":"/method/batch-normalization","method":"Batch Normalization","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":33,"pages_in_order":63,"rows_per_page":100,"rows":[3201,3300],"of":6287,"counts":{"archive_papers_tagged":6287,"with_a_code_link":2771,"where_syntology_ran_a_sample":742,"not_listed_spam_title":0,"listed":6287,"listed_where_code_ran":742,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":627,"every_run_a_failure_of_syntologys_instrument":115,"listed_with_a_run_with_no_instrument_failure":627,"listed_every_run_a_failure_of_syntologys_instrument":115,"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/batch-normalization","prev":"/method/batch-normalization/papers/32","next":"/method/batch-normalization/papers/34","papers":[{"paper":"/paper/mutually-improved-endoscopic-image-synthesis","slug":"mutually-improved-endoscopic-image-synthesis","title":"Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation","date":"2021-07-14","arxiv_id":"2107.06941","n_code_links":1,"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":"a-deep-reinforcement-learning-approach-for-6","title":"A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization","date":"2021-07-13","arxiv_id":"2107.06115","n_code_links":0,"syntology":null},{"paper":"/paper/cmt-convolutional-neural-networks-meet-vision","slug":"cmt-convolutional-neural-networks-meet-vision","title":"CMT: Convolutional Neural Networks Meet Vision Transformers","date":"2021-07-13","arxiv_id":"2107.06263","n_code_links":14,"syntology":{"ran":4,"of":9,"n_ran_checked":3,"n_instrument":1,"unverified":5,"pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","official":{"repos":["ggjy/cmt.pytorch"],"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":"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":"hant-hardware-aware-network-transformation","title":"LANA: Latency Aware Network Acceleration","date":"2021-07-12","arxiv_id":"2107.10624","n_code_links":0,"syntology":null},{"paper":"/paper/real-time-super-resolution-system-of-4k-video","slug":"real-time-super-resolution-system-of-4k-video","title":"Real-Time Super-Resolution System of 4K-Video Based on Deep Learning","date":"2021-07-12","arxiv_id":"2107.05307","n_code_links":1,"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":"training-deep-cross-modality-conversion","title":"Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion","date":"2021-07-12","arxiv_id":"2107.05238","n_code_links":0,"syntology":null},{"paper":null,"slug":"dense-sparse-deep-cnn-training-for-image","title":"Dense-Sparse Deep Convolutional Neural Networks Training for Image Denoising","date":"2021-07-10","arxiv_id":"2107.04857","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/collaboration-of-experts-achieving-80-top-1","slug":"collaboration-of-experts-achieving-80-top-1","title":"Collaboration of Experts: Achieving 80% Top-1 Accuracy on ImageNet with 100M FLOPs","date":"2021-07-08","arxiv_id":"2107.03815","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":"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/combining-efficientnet-and-vision","slug":"combining-efficientnet-and-vision","title":"Combining EfficientNet and Vision Transformers for Video Deepfake Detection","date":"2021-07-06","arxiv_id":"2107.02612","n_code_links":3,"syntology":{"ran":9,"of":14,"n_ran_checked":6,"n_instrument":3,"unverified":5,"pointer_only":4,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 2 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","official":{"repos":["davide-coccomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection","davide-coccomini/Combining-EfficientNet-and-Vision-Transformersfor-Video-Deepfake-Detection"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"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":"/paper/provable-lipschitz-certification-for","slug":"provable-lipschitz-certification-for","title":"Provable Lipschitz Certification for Generative Models","date":"2021-07-06","arxiv_id":"2107.02732","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":"/paper/gaze-estimation-with-an-ensemble-of-four","slug":"gaze-estimation-with-an-ensemble-of-four","title":"Gaze Estimation with an Ensemble of Four Architectures","date":"2021-07-05","arxiv_id":"2107.01980","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/divergentnets-medical-image-segmentation-by","slug":"divergentnets-medical-image-segmentation-by","title":"DivergentNets: Medical Image Segmentation by Network Ensemble","date":"2021-07-01","arxiv_id":"2107.00283","n_code_links":1,"syntology":null},{"paper":null,"slug":"unsupervised-model-drift-estimation-with","title":"Unsupervised Model Drift Estimation with Batch Normalization Statistics for Dataset Shift Detection and Model Selection","date":"2021-07-01","arxiv_id":"2107.00191","n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-based-secure-noma-and-cognitive-radio","title":"AI-Based Secure NOMA and Cognitive Radio enabled Green Communications: Channel State Information and Battery Value Uncertainties","date":"2021-06-30","arxiv_id":"2106.15964","n_code_links":0,"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":"/paper/on-the-periodic-behavior-of-neural-network","slug":"on-the-periodic-behavior-of-neural-network","title":"On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay","date":"2021-06-29","arxiv_id":"2106.15739","n_code_links":1,"syntology":{"ran":7,"of":11,"n_ran_checked":6,"n_instrument":1,"unverified":4,"pointer_only":0,"phrase":"7 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; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["tipt0p/periodic_behavior_bn_wd"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"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":"understanding-cognitive-fatigue-from-fmri","title":"Understanding Cognitive Fatigue from fMRI Scans with Self-supervised Learning","date":"2021-06-28","arxiv_id":"2106.15009","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-dynamics-of-nonlinear","title":"Understanding Dynamics of Nonlinear Representation Learning and Its Application","date":"2021-06-28","arxiv_id":"2106.14836","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-machine-learning-model-for-early-detection","title":"A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Images","date":"2021-06-27","arxiv_id":"2106.14207","n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-based-presentation-creator-with-customized","title":"AI based Presentation Creator With Customized Audio Content Delivery","date":"2021-06-27","arxiv_id":"2106.14213","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":null,"slug":"image-to-image-transformation-with-auxiliary","title":"Image-to-image Transformation with Auxiliary Condition","date":"2021-06-25","arxiv_id":"2106.13696","n_code_links":0,"syntology":null},{"paper":null,"slug":"ladder-polynomial-neural-networks","title":"Ladder Polynomial Neural Networks","date":"2021-06-25","arxiv_id":"2106.13834","n_code_links":0,"syntology":null},{"paper":"/paper/ranger21-a-synergistic-deep-learning","slug":"ranger21-a-synergistic-deep-learning","title":"Ranger21: a synergistic deep learning optimizer","date":"2021-06-25","arxiv_id":"2106.13731","n_code_links":2,"syntology":null},{"paper":null,"slug":"perceptually-guided-adversarial-perturbations","title":"Can Perceptual Guidance Lead to Semantically Explainable Adversarial Perturbations?","date":"2021-06-24","arxiv_id":"2106.12731","n_code_links":0,"syntology":null},{"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/feature-alignment-for-approximated","slug":"feature-alignment-for-approximated","title":"Feature Alignment as a Generative Process","date":"2021-06-23","arxiv_id":"2106.12562","n_code_links":2,"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":"wallpaper-texture-generation-and-style","title":"Wallpaper Texture Generation and Style Transfer Based on Multi-label Semantics","date":"2021-06-22","arxiv_id":"2106.11482","n_code_links":0,"syntology":null},{"paper":"/paper/compressing-deep-ode-nets-using-basis","slug":"compressing-deep-ode-nets-using-basis","title":"Stateful ODE-Nets using Basis Function Expansions","date":"2021-06-21","arxiv_id":"2106.10820","n_code_links":3,"syntology":null},{"paper":null,"slug":"constructing-forest-biomass-prediction-maps","title":"On the potential of sequential and non-sequential regression models for Sentinel-1-based biomass prediction in Tanzanian miombo forests","date":"2021-06-21","arxiv_id":"2106.15020","n_code_links":0,"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":"/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":"communication-efficient-sgd-via-gradient","title":"Communication Efficient SGD via Gradient Sampling With Bayes Prior","date":"2021-06-19","arxiv_id":null,"n_code_links":0,"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":"improving-multiple-object-tracking-with-1","title":"Improving Multiple Object Tracking With Single Object Tracking","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/one-to-many-approach-for-improving-super","slug":"one-to-many-approach-for-improving-super","title":"One-to-many Approach for Improving Super-Resolution","date":"2021-06-19","arxiv_id":"2106.10437","n_code_links":1,"syntology":null},{"paper":null,"slug":"representative-batch-normalization-with","title":"Representative Batch Normalization With Feature Calibration","date":"2021-06-19","arxiv_id":null,"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},{"paper":null,"slug":"direct-reconstruction-of-linear-parametric","title":"Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior","date":"2021-06-18","arxiv_id":"2106.10359","n_code_links":0,"syntology":null},{"paper":"/paper/proper-value-equivalence","slug":"proper-value-equivalence","title":"Proper Value Equivalence","date":"2021-06-18","arxiv_id":"2106.10316","n_code_links":1,"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":"a-reinforcement-learning-approach-for-an-irs","title":"A Reinforcement Learning Approach for an IRS-assisted NOMA Network","date":"2021-06-17","arxiv_id":"2106.09611","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-reinforcement-learning-based-1","title":"Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks","date":"2021-06-17","arxiv_id":"2106.09616","n_code_links":0,"syntology":null},{"paper":null,"slug":"federated-cyclegan-for-privacy-preserving","title":"Federated CycleGAN for Privacy-Preserving Image-to-Image Translation","date":"2021-06-17","arxiv_id":"2106.09246","n_code_links":0,"syntology":null},{"paper":"/paper/layer-folding-neural-network-depth-reduction","slug":"layer-folding-neural-network-depth-reduction","title":"Layer Folding: Neural Network Depth Reduction using Activation Linearization","date":"2021-06-17","arxiv_id":"2106.09309","n_code_links":1,"syntology":{"ran":9,"of":12,"n_ran_checked":9,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["LayerFolding/Layer-Folding"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"many-agent-reinforcement-learning-under","title":"Many Agent Reinforcement Learning Under Partial Observability","date":"2021-06-17","arxiv_id":"2106.09825","n_code_links":0,"syntology":null},{"paper":null,"slug":"orthogonal-pade-activation-functions","title":"Orthogonal-Padé Activation Functions: Trainable Activation functions for smooth and faster convergence in deep networks","date":"2021-06-17","arxiv_id":"2106.09693","n_code_links":0,"syntology":null},{"paper":"/paper/xcit-cross-covariance-image-transformers","slug":"xcit-cross-covariance-image-transformers","title":"XCiT: Cross-Covariance Image Transformers","date":"2021-06-17","arxiv_id":"2106.09681","n_code_links":12,"syntology":{"ran":10,"of":14,"n_ran_checked":10,"n_instrument":0,"unverified":4,"pointer_only":11,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["facebookresearch/xcit","rwightman/pytorch-image-models"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"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":"a-lightweight-relu-based-feature-fusion-for","title":"A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification","date":"2021-06-15","arxiv_id":"2106.07879","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":null,"slug":"ctrl-p-temporal-control-of-prosodic-variation","title":"Ctrl-P: Temporal Control of Prosodic Variation for Speech Synthesis","date":"2021-06-15","arxiv_id":"2106.08352","n_code_links":0,"syntology":null},{"paper":"/paper/generating-thermal-human-faces-for","slug":"generating-thermal-human-faces-for","title":"Generating Thermal Human Faces for Physiological Assessment Using Thermal Sensor Auxiliary Labels","date":"2021-06-15","arxiv_id":"2106.08091","n_code_links":1,"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/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":null,"slug":"pyramidal-dense-attention-networks-for","title":"Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution","date":"2021-06-13","arxiv_id":"2106.06996","n_code_links":0,"syntology":null},{"paper":"/paper/cartl-cooperative-adversarially-robust","slug":"cartl-cooperative-adversarially-robust","title":"CARTL: Cooperative Adversarially-Robust Transfer Learning","date":"2021-06-12","arxiv_id":"2106.06667","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"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":["NISP-official/CARTL"],"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/hr-nas-searching-efficient-high-resolution","slug":"hr-nas-searching-efficient-high-resolution","title":"HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers","date":"2021-06-11","arxiv_id":"2106.06560","n_code_links":1,"syntology":{"ran":10,"of":12,"n_ran_checked":8,"n_instrument":2,"unverified":2,"pointer_only":1,"phrase":"10 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; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["dingmyu/HR-NAS"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"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":"/paper/revisiting-contrastive-methods-for","slug":"revisiting-contrastive-methods-for","title":"Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations","date":"2021-06-10","arxiv_id":"2106.05967","n_code_links":2,"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":"vector-quantized-models-for-planning","title":"Vector Quantized Models for Planning","date":"2021-06-08","arxiv_id":"2106.04615","n_code_links":0,"syntology":null},{"paper":null,"slug":"high-resolution-solar-image-generation-using","title":"High Resolution Solar Image Generation using Generative Adversarial Networks","date":"2021-06-07","arxiv_id":"2106.03814","n_code_links":0,"syntology":null},{"paper":null,"slug":"making-efficientnet-more-efficient-exploring","title":"Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution Training","date":"2021-06-07","arxiv_id":"2106.03640","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}],"record_sha256":"bd06a0193db65e6aabfdc559447e32dc4cbfb4c9eb20a2e30ab1eb232480ef79","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}