{"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/efficientnet/papers/2","list_of":"/method/efficientnet","method":"EfficientNet","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":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,195],"of":195,"counts":{"archive_papers_tagged":195,"with_a_code_link":101,"where_syntology_ran_a_sample":20,"not_listed_spam_title":0,"listed":195,"listed_where_code_ran":20,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":15,"every_run_a_failure_of_syntologys_instrument":5,"listed_with_a_run_with_no_instrument_failure":15,"listed_every_run_a_failure_of_syntologys_instrument":5,"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/efficientnet","prev":"/method/efficientnet","next":null,"papers":[{"paper":"/paper/plot2api-recommending-graphic-api-from-plot","slug":"plot2api-recommending-graphic-api-from-plot","title":"Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network","date":"2021-04-02","arxiv_id":"2104.01032","n_code_links":1,"syntology":null},{"paper":"/paper/one-shot-neural-ensemble-architecture-search","slug":"one-shot-neural-ensemble-architecture-search","title":"One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking","date":"2021-04-01","arxiv_id":"2104.00597","n_code_links":1,"syntology":null},{"paper":"/paper/facial-expression-and-attributes-recognition","slug":"facial-expression-and-attributes-recognition","title":"Facial expression and attributes recognition based on multi-task learning of lightweight neural networks","date":"2021-03-31","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/facial-expression-and-attributes-recognition-1","slug":"facial-expression-and-attributes-recognition-1","title":"Facial expression and attributes recognition based on multi-task learning of lightweight neural networks","date":"2021-03-31","arxiv_id":"2103.17107","n_code_links":2,"syntology":null},{"paper":null,"slug":"differentiable-network-adaption-with-elastic","title":"Differentiable Network Adaption with Elastic Search Space","date":"2021-03-30","arxiv_id":"2103.16350","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-multi-objective-optimization-for","slug":"efficient-multi-objective-optimization-for","title":"Scalable Pareto Front Approximation for Deep Multi-Objective Learning","date":"2021-03-24","arxiv_id":"2103.13392","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ruchtem/cosmos"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/bossnas-exploring-hybrid-cnn-transformers","slug":"bossnas-exploring-hybrid-cnn-transformers","title":"BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search","date":"2021-03-23","arxiv_id":"2103.12424","n_code_links":1,"syntology":null},{"paper":"/paper/danish-fungi-2020-not-just-another-image","slug":"danish-fungi-2020-not-just-another-image","title":"Danish Fungi 2020 -- Not Just Another Image Recognition Dataset","date":"2021-03-18","arxiv_id":"2103.10107","n_code_links":1,"syntology":null},{"paper":"/paper/revisiting-resnets-improved-training-and","slug":"revisiting-resnets-improved-training-and","title":"Revisiting ResNets: Improved Training and Scaling Strategies","date":"2021-03-13","arxiv_id":"2103.07579","n_code_links":3,"syntology":null},{"paper":"/paper/enhancing-sensor-resolution-improves-cnn","slug":"enhancing-sensor-resolution-improves-cnn","title":"Enhancing sensor resolution improves CNN accuracy given the same number of parameters or FLOPS","date":"2021-03-09","arxiv_id":"2103.05251","n_code_links":1,"syntology":null},{"paper":"/paper/webface260m-a-benchmark-unveiling-the-power","slug":"webface260m-a-benchmark-unveiling-the-power","title":"WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition","date":"2021-03-06","arxiv_id":"2103.04098","n_code_links":0,"syntology":null},{"paper":"/paper/coordinate-attention-for-efficient-mobile","slug":"coordinate-attention-for-efficient-mobile","title":"Coordinate Attention for Efficient Mobile Network Design","date":"2021-03-04","arxiv_id":"2103.02907","n_code_links":2,"syntology":null},{"paper":null,"slug":"robust-pollen-imagery-classification-with","title":"Robust Pollen Imagery Classification with Generative Modeling and Mixup Training","date":"2021-02-25","arxiv_id":"2102.13143","n_code_links":0,"syntology":null},{"paper":"/paper/densely-nested-top-down-flows-for-salient","slug":"densely-nested-top-down-flows-for-salient","title":"Densely Nested Top-Down Flows for Salient Object Detection","date":"2021-02-18","arxiv_id":"2102.09133","n_code_links":1,"syntology":null},{"paper":null,"slug":"rethinking-co-design-of-neural-architectures","title":"Rethinking Co-design of Neural Architectures and Hardware Accelerators","date":"2021-02-17","arxiv_id":"2102.08619","n_code_links":0,"syntology":null},{"paper":null,"slug":"searching-for-fast-model-families-on","title":"Searching for Fast Model Families on Datacenter Accelerators","date":"2021-02-10","arxiv_id":"2102.05610","n_code_links":0,"syntology":null},{"paper":"/paper/psla-improving-audio-event-classification","slug":"psla-improving-audio-event-classification","title":"PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation","date":"2021-02-02","arxiv_id":"2102.01243","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-models-for-calculation-of","title":"Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly","date":"2021-01-19","arxiv_id":"2101.07606","n_code_links":0,"syntology":null},{"paper":"/paper/repvgg-making-vgg-style-convnets-great-again","slug":"repvgg-making-vgg-style-convnets-great-again","title":"RepVGG: Making VGG-style ConvNets Great Again","date":"2021-01-11","arxiv_id":"2101.03697","n_code_links":25,"syntology":{"ran":13,"of":16,"n_ran_checked":8,"n_instrument":5,"unverified":3,"pointer_only":6,"phrase":"13 ran (of which 7 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","official":{"repos":["DingXiaoH/RepVGG"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":null,"slug":"exploring-adversarial-fake-images-on-face","title":"Exploring Adversarial Fake Images on Face Manifold","date":"2021-01-09","arxiv_id":"2101.03272","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-certified-robustness-of-deep","title":"Boosting Certified Robustness of Deep Networks via a Compositional Architecture","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-adversarial-neural-architecture","title":"Generative Adversarial Neural Architecture Search with Importance Sampling","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"making-coherence-out-of-nothing-at-all-1","title":"Making Coherence Out of Nothing At All: Measuring Evolution of Gradient Alignment","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"nahas-neural-architecture-and-hardware","title":"NAHAS: Neural Architecture and Hardware Accelerator Search","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"diabetic-retinopathy-grading-system-based-on","title":"Diabetic Retinopathy Grading System Based on Transfer Learning","date":"2020-12-23","arxiv_id":"2012.12515","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-and-visualizable-convolutional","slug":"efficient-and-visualizable-convolutional","title":"Efficient and Visualizable Convolutional Neural Networks for COVID-19 Classification Using Chest CT","date":"2020-12-22","arxiv_id":"2012.11860","n_code_links":1,"syntology":null},{"paper":"/paper/objectron-a-large-scale-dataset-of-object","slug":"objectron-a-large-scale-dataset-of-object","title":"Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations","date":"2020-12-18","arxiv_id":"2012.09988","n_code_links":1,"syntology":null},{"paper":"/paper/simple-copy-paste-is-a-strong-data","slug":"simple-copy-paste-is-a-strong-data","title":"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation","date":"2020-12-13","arxiv_id":"2012.07177","n_code_links":5,"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":["tensorflow/tpu"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"multiple-networks-are-more-efficient-than-one","title":"Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models","date":"2020-12-03","arxiv_id":"2012.01988","n_code_links":0,"syntology":null},{"paper":"/paper/model-rubiks-cube-twisting-resolution-depth","slug":"model-rubiks-cube-twisting-resolution-depth","title":"Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets","date":"2020-12-01","arxiv_id":null,"n_code_links":3,"syntology":null},{"paper":null,"slug":"semi-supervised-noisy-student-pre-training-on","title":"Semi-Supervised Noisy Student Pre-training on EfficientNet Architectures for Plant Pathology Classification","date":"2020-12-01","arxiv_id":"2012.00332","n_code_links":0,"syntology":null},{"paper":"/paper/adabins-depth-estimation-using-adaptive-bins","slug":"adabins-depth-estimation-using-adaptive-bins","title":"AdaBins: Depth Estimation using Adaptive Bins","date":"2020-11-28","arxiv_id":"2011.14141","n_code_links":11,"syntology":null},{"paper":null,"slug":"depth-enhanced-feature-pyramid-network-for","title":"Depth-Enhanced Feature Pyramid Network for Occlusion-Aware Verification of Buildings from Oblique Images","date":"2020-11-26","arxiv_id":"2011.13226","n_code_links":0,"syntology":null},{"paper":null,"slug":"imagenet-pretrained-cnns-for-jpeg","title":"ImageNet Pretrained CNNs for JPEG Steganalysis","date":"2020-11-24","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-embedding-network-for-3d-brain","title":"Efficient embedding network for 3D brain tumor segmentation","date":"2020-11-22","arxiv_id":"2011.11052","n_code_links":0,"syntology":null},{"paper":null,"slug":"fp-nas-fast-probabilistic-neural-architecture","title":"FP-NAS: Fast Probabilistic Neural Architecture Search","date":"2020-11-22","arxiv_id":"2011.10949","n_code_links":0,"syntology":null},{"paper":"/paper/optic-disc-cup-and-fovea-detection-from","slug":"optic-disc-cup-and-fovea-detection-from","title":"Optic Disc, Cup and Fovea Detection from Retinal Images Using U-Net++ with EfficientNet Encoder","date":"2020-11-20","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/an-efficient-and-scalable-deep-learning","slug":"an-efficient-and-scalable-deep-learning","title":"An Efficient and Scalable Deep Learning Approach for Road Damage Detection","date":"2020-11-18","arxiv_id":"2011.09577","n_code_links":2,"syntology":null},{"paper":null,"slug":"layer-wise-data-free-cnn-compression","title":"Layer-Wise Data-Free CNN Compression","date":"2020-11-18","arxiv_id":"2011.09058","n_code_links":0,"syntology":null},{"paper":"/paper/padim-a-patch-distribution-modeling-framework","slug":"padim-a-patch-distribution-modeling-framework","title":"PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization","date":"2020-11-17","arxiv_id":"2011.08785","n_code_links":26,"syntology":{"ran":7,"of":7,"n_ran_checked":2,"n_instrument":5,"unverified":0,"pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"predictive-analysis-of-diabetic-retinopathy","title":"Predictive Analysis of Diabetic Retinopathy with Transfer Learning","date":"2020-11-08","arxiv_id":"2011.04052","n_code_links":0,"syntology":null},{"paper":"/paper/deep-transfer-learning-for-automated","slug":"deep-transfer-learning-for-automated","title":"Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs","date":"2020-11-06","arxiv_id":"2011.04475","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-forchheim-image-database-for-camera","title":"The Forchheim Image Database for Camera Identification in the Wild","date":"2020-11-04","arxiv_id":"2011.02241","n_code_links":0,"syntology":null},{"paper":null,"slug":"83-imagenet-accuracy-in-one-hour","title":"Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour","date":"2020-10-30","arxiv_id":"2011.00071","n_code_links":0,"syntology":null},{"paper":"/paper/cream-of-the-crop-distilling-prioritized","slug":"cream-of-the-crop-distilling-prioritized","title":"Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search","date":"2020-10-29","arxiv_id":"2010.15821","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["microsoft/cream"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/model-rubik-s-cube-twisting-resolution-depth","slug":"model-rubik-s-cube-twisting-resolution-depth","title":"Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets","date":"2020-10-28","arxiv_id":"2010.14819","n_code_links":9,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["huawei-noah/CV-backbones","huawei-noah/ghostnet"],"state":"official: harvested for another paper","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":"/paper/deep-learning-frameworks-for-pavement","slug":"deep-learning-frameworks-for-pavement","title":"Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis","date":"2020-10-21","arxiv_id":"2010.10681","n_code_links":1,"syntology":null},{"paper":"/paper/classification-and-understanding-of-cloud","slug":"classification-and-understanding-of-cloud","title":"Classification and understanding of cloud structures via satellite images with EfficientUNet","date":"2020-09-27","arxiv_id":"2009.12931","n_code_links":1,"syntology":null},{"paper":null,"slug":"ecovnet-an-ensemble-of-deep-convolutional","title":"ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays","date":"2020-09-24","arxiv_id":"2009.11850","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-exposure-attack-on-diabetic","title":"Adversarial Exposure Attack on Diabetic Retinopathy Imagery Grading","date":"2020-09-19","arxiv_id":"2009.09231","n_code_links":0,"syntology":null},{"paper":"/paper/image-captioning-with-attention-for-smart","slug":"image-captioning-with-attention-for-smart","title":"Image Captioning with Attention for Smart Local Tourism using EfficientNet","date":"2020-09-18","arxiv_id":"2009.08899","n_code_links":1,"syntology":null},{"paper":"/paper/efficientnet-elite-extremely-lightweight-and","slug":"efficientnet-elite-extremely-lightweight-and","title":"EfficientNet-eLite: Extremely Lightweight and Efficient CNN Models for Edge Devices by Network Candidate Search","date":"2020-09-16","arxiv_id":"2009.07409","n_code_links":3,"syntology":null},{"paper":"/paper/fastsal-a-computationally-efficient-network","slug":"fastsal-a-computationally-efficient-network","title":"FastSal: a Computationally Efficient Network for Visual Saliency Prediction","date":"2020-08-25","arxiv_id":"2008.11151","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"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":["feiyanhu/FastSal"],"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-improved-person-re-identification-method","slug":"an-improved-person-re-identification-method","title":"An Improved Person Re-identification Method by light-weight convolutional neural network","date":"2020-08-21","arxiv_id":"2008.09448","n_code_links":1,"syntology":null},{"paper":"/paper/the-ensemble-method-for-thorax-diseases","slug":"the-ensemble-method-for-thorax-diseases","title":"An Aggregate Method for Thorax Diseases Classification","date":"2020-08-07","arxiv_id":"2008.03008","n_code_links":1,"syntology":null},{"paper":null,"slug":"structured-convolutions-for-efficient-neural","title":"Structured Convolutions for Efficient Neural Network Design","date":"2020-08-06","arxiv_id":"2008.02454","n_code_links":0,"syntology":null},{"paper":"/paper/efficienthrnet-efficient-scaling-for","slug":"efficienthrnet-efficient-scaling-for","title":"EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation","date":"2020-07-16","arxiv_id":"2007.08090","n_code_links":2,"syntology":null},{"paper":"/paper/improving-accuracy-and-speeding-up-document","slug":"improving-accuracy-and-speeding-up-document","title":"Improving accuracy and speeding up Document Image Classification through parallel systems","date":"2020-06-16","arxiv_id":"2006.09141","n_code_links":1,"syntology":null},{"paper":null,"slug":"autohas-differentiable-hyper-parameter-and","title":"AutoHAS: Efficient Hyperparameter and Architecture Search","date":"2020-06-05","arxiv_id":"2006.03656","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-based-diagnosis-of-covid-19","slug":"deep-learning-based-diagnosis-of-covid-19","title":"Deep Learning based Diagnosis of COVID-19 usingChest CT-scan Images","date":"2020-05-20","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/ai-augmentation-of-radiologist-performance-in","slug":"ai-augmentation-of-radiologist-performance-in","title":"AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT","date":"2020-04-27","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-limits-to-multi-modal-popularity","title":"On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline","date":"2020-04-26","arxiv_id":"2004.12482","n_code_links":0,"syntology":null},{"paper":"/paper/efficientpose-scalable-single-person-pose","slug":"efficientpose-scalable-single-person-pose","title":"EfficientPose: Scalable single-person pose estimation","date":"2020-04-25","arxiv_id":"2004.12186","n_code_links":1,"syntology":null},{"paper":"/paper/yolov4-optimal-speed-and-accuracy-of-object","slug":"yolov4-optimal-speed-and-accuracy-of-object","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","date":"2020-04-23","arxiv_id":"2004.10934","n_code_links":223,"syntology":{"ran":142,"of":184,"n_ran_checked":133,"n_instrument":9,"unverified":42,"pointer_only":21,"phrase":"142 ran (of which 0 constructed an object rather than computing a result; 133 with no instrument failure: 6 honoured, 0 violated, 127 with no contract checked; 9 where Syntology's instrument failed) · 42 unverified","official":{"repos":["AlexeyAB/darknet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/lsq-improving-low-bit-quantization-through","slug":"lsq-improving-low-bit-quantization-through","title":"LSQ+: Improving low-bit quantization through learnable offsets and better initialization","date":"2020-04-20","arxiv_id":"2004.09576","n_code_links":4,"syntology":{"ran":5,"of":10,"n_ran_checked":4,"n_instrument":1,"unverified":5,"pointer_only":10,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","official":null}},{"paper":"/paper/resnest-split-attention-networks","slug":"resnest-split-attention-networks","title":"ResNeSt: Split-Attention Networks","date":"2020-04-19","arxiv_id":"2004.08955","n_code_links":36,"syntology":{"ran":28,"of":48,"n_ran_checked":25,"n_instrument":3,"unverified":20,"pointer_only":23,"phrase":"28 ran (of which 0 constructed an object rather than computing a result; 25 with no instrument failure: 0 honoured, 0 violated, 25 with no contract checked; 3 where Syntology's instrument failed) · 20 unverified","official":{"repos":["zhanghang1989/ResNeSt"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/driftnet-aggressive-driving-behavior","slug":"driftnet-aggressive-driving-behavior","title":"DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet Architecture","date":"2020-04-18","arxiv_id":"2004.11970","n_code_links":1,"syntology":null},{"paper":"/paper/video-face-manipulation-detection-through","slug":"video-face-manipulation-detection-through","title":"Video Face Manipulation Detection Through Ensemble of CNNs","date":"2020-04-16","arxiv_id":"2004.07676","n_code_links":3,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"pointer_only":3,"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) · 2 unverified","official":{"repos":["polimi-ispl/icpr2020dfdc"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/an-evaluation-of-dnn-architectures-for-page","slug":"an-evaluation-of-dnn-architectures-for-page","title":"An Evaluation of DNN Architectures for Page Segmentation of Historical Newspapers","date":"2020-04-15","arxiv_id":"2004.07317","n_code_links":1,"syntology":null},{"paper":"/paper/towards-an-efficient-deep-learning-model-for","slug":"towards-an-efficient-deep-learning-model-for","title":"Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images","date":"2020-04-12","arxiv_id":"2004.05717","n_code_links":2,"syntology":null},{"paper":"/paper/analysis-on-deeplabv3-performance-for","slug":"analysis-on-deeplabv3-performance-for","title":"Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection","date":"2020-04-09","arxiv_id":"2004.04822","n_code_links":1,"syntology":null},{"paper":"/paper/evolving-normalization-activation-layers","slug":"evolving-normalization-activation-layers","title":"Evolving Normalization-Activation Layers","date":"2020-04-06","arxiv_id":"2004.02967","n_code_links":8,"syntology":{"ran":3,"of":5,"n_ran_checked":0,"n_instrument":3,"unverified":2,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/nbdt-neural-backed-decision-trees","slug":"nbdt-neural-backed-decision-trees","title":"NBDT: Neural-Backed Decision Trees","date":"2020-04-01","arxiv_id":"2004.00221","n_code_links":2,"syntology":{"ran":18,"of":29,"n_ran_checked":4,"n_instrument":14,"unverified":11,"pointer_only":0,"phrase":"18 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; 14 where Syntology's instrument failed) · 11 unverified","official":{"repos":["alvinwan/neural-backed-decision-trees"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/circumventing-outliers-of-autoaugment-with","slug":"circumventing-outliers-of-autoaugment-with","title":"Circumventing Outliers of AutoAugment with Knowledge Distillation","date":"2020-03-25","arxiv_id":"2003.11342","n_code_links":1,"syntology":null},{"paper":"/paper/meta-pseudo-labels","slug":"meta-pseudo-labels","title":"Meta Pseudo Labels","date":"2020-03-23","arxiv_id":"2003.10580","n_code_links":9,"syntology":{"ran":12,"of":14,"n_ran_checked":10,"n_instrument":2,"unverified":2,"pointer_only":3,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["google-research/google-research"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/multi-plateau-ensemble-for-endoscopic","slug":"multi-plateau-ensemble-for-endoscopic","title":"Multi-Plateau Ensemble for Endoscopic Artefact Segmentation and Detection","date":"2020-03-23","arxiv_id":"2003.10129","n_code_links":1,"syntology":null},{"paper":"/paper/fixing-the-train-test-resolution-discrepancy-2","slug":"fixing-the-train-test-resolution-discrepancy-2","title":"Fixing the train-test resolution discrepancy: FixEfficientNet","date":"2020-03-18","arxiv_id":"2003.08237","n_code_links":1,"syntology":null},{"paper":"/paper/gimme-signals-discriminative-signal-encoding","slug":"gimme-signals-discriminative-signal-encoding","title":"Gimme Signals: Discriminative signal encoding for multimodal activity recognition","date":"2020-03-13","arxiv_id":"2003.06156","n_code_links":2,"syntology":null},{"paper":null,"slug":"learned-threshold-pruning","title":"Learned Threshold Pruning","date":"2020-02-28","arxiv_id":"2003.00075","n_code_links":0,"syntology":null},{"paper":"/paper/maxup-a-simple-way-to-improve-generalization","slug":"maxup-a-simple-way-to-improve-generalization","title":"MaxUp: A Simple Way to Improve Generalization of Neural Network Training","date":"2020-02-20","arxiv_id":"2002.09024","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":null}},{"paper":"/paper/efficient-model-for-image-classification-with","slug":"efficient-model-for-image-classification-with","title":"Efficient Model for Image Classification With Regularization Tricks","date":"2020-02-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"attention-based-face-antispoofing-of-rgb","title":"Attention-Based Face AntiSpoofing of RGB Images, using a Minimal End-2-End Neural Network","date":"2019-12-18","arxiv_id":"1912.08870","n_code_links":0,"syntology":null},{"paper":null,"slug":"structured-multi-hashing-for-model","title":"Structured Multi-Hashing for Model Compression","date":"2019-11-25","arxiv_id":"1911.11177","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-examples-improve-image","slug":"adversarial-examples-improve-image","title":"Adversarial Examples Improve Image Recognition","date":"2019-11-21","arxiv_id":"1911.09665","n_code_links":6,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["tensorflow/tpu"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/fast-sparse-convnets-1","slug":"fast-sparse-convnets-1","title":"Fast Sparse ConvNets","date":"2019-11-21","arxiv_id":"1911.09723","n_code_links":5,"syntology":null},{"paper":"/paper/efficientdet-scalable-and-efficient-object","slug":"efficientdet-scalable-and-efficient-object","title":"EfficientDet: Scalable and Efficient Object Detection","date":"2019-11-20","arxiv_id":"1911.09070","n_code_links":64,"syntology":{"ran":55,"of":70,"n_ran_checked":48,"n_instrument":7,"unverified":15,"pointer_only":7,"phrase":"55 ran (of which 1 constructed an object rather than computing a result; 48 with no instrument failure: 4 honoured, 0 violated, 44 with no contract checked; 7 where Syntology's instrument failed) · 15 unverified","official":{"repos":["google/automl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"experimental-exploration-of-compact","title":"Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection","date":"2019-11-20","arxiv_id":"1911.09010","n_code_links":0,"syntology":null},{"paper":"/paper/self-training-with-noisy-student-improves","slug":"self-training-with-noisy-student-improves","title":"Self-training with Noisy Student improves ImageNet classification","date":"2019-11-11","arxiv_id":"1911.04252","n_code_links":13,"syntology":{"ran":13,"of":24,"n_ran_checked":10,"n_instrument":3,"unverified":11,"pointer_only":2,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 2 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 11 unverified","official":{"repos":["google-research/noisystudent","tensorflow/tpu"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":10,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"identification-of-primary-angle-closure-on-as","title":"Identification of primary angle-closure on AS-OCT images with Convolutional Neural Networks","date":"2019-10-23","arxiv_id":"1910.10414","n_code_links":0,"syntology":null},{"paper":null,"slug":"icps-net-an-end-to-end-rgb-based-indoor","title":"ICPS-net: An End-to-End RGB-based Indoor Camera Positioning System using deep convolutional neural networks","date":"2019-10-14","arxiv_id":"1910.06219","n_code_links":0,"syntology":null},{"paper":"/paper/randaugment-practical-data-augmentation-with","slug":"randaugment-practical-data-augmentation-with","title":"RandAugment: Practical automated data augmentation with a reduced search space","date":"2019-09-30","arxiv_id":"1909.13719","n_code_links":19,"syntology":{"ran":58,"of":65,"n_ran_checked":7,"n_instrument":51,"unverified":7,"pointer_only":17,"phrase":"58 ran (of which 1 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 51 where Syntology's instrument failed) · 7 unverified","official":null}},{"paper":"/paper/a-closer-look-at-network-resolution-for","slug":"a-closer-look-at-network-resolution-for","title":"MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution","date":"2019-09-27","arxiv_id":"1909.12978","n_code_links":2,"syntology":null},{"paper":"/paper/pretraining-boosts-out-of-domain-robustness","slug":"pretraining-boosts-out-of-domain-robustness","title":"Pretraining boosts out-of-domain robustness for pose estimation","date":"2019-09-24","arxiv_id":"1909.11229","n_code_links":1,"syntology":null},{"paper":"/paper/efficientnet-rethinking-model-scaling-for","slug":"efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","arxiv_id":"1905.11946","n_code_links":144,"syntology":{"ran":198,"of":302,"n_ran_checked":157,"n_instrument":41,"unverified":104,"pointer_only":113,"phrase":"198 ran (of which 73 constructed an object rather than computing a result; 157 with no instrument failure: 26 honoured, 2 violated, 129 with no contract checked; 41 where Syntology's instrument failed) · 104 unverified","official":{"repos":["tensorflow/tpu"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":"/paper/soft-conditional-computation","slug":"soft-conditional-computation","title":"CondConv: Conditionally Parameterized Convolutions for Efficient Inference","date":"2019-04-10","arxiv_id":"1904.04971","n_code_links":9,"syntology":null}],"record_sha256":"65d69cb3614799d192e81b7122a79fedfa24ba7868b407cec5494f3ab270a6cf","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}