{"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/1x1-convolution/papers/49","list_of":"/method/1x1-convolution","method":"1x1 Convolution","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":49,"pages_in_order":57,"rows_per_page":100,"rows":[4801,4900],"of":5640,"counts":{"archive_papers_tagged":5640,"with_a_code_link":2516,"where_syntology_ran_a_sample":651,"not_listed_spam_title":0,"listed":5640,"listed_where_code_ran":651,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":545,"every_run_a_failure_of_syntologys_instrument":106,"listed_with_a_run_with_no_instrument_failure":545,"listed_every_run_a_failure_of_syntologys_instrument":106,"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/1x1-convolution","prev":"/method/1x1-convolution/papers/48","next":"/method/1x1-convolution/papers/50","papers":[{"paper":null,"slug":"dc-al-gan-pseudoprogression-and-true-tumor","title":"DC-AL GAN: Pseudoprogression and True Tumor Progression of Glioblastoma Multiform Image Classification Based on DCGAN and AlexNet","date":"2019-02-16","arxiv_id":"1902.06085","n_code_links":0,"syntology":null},{"paper":"/paper/res-se-net-boosting-performance-of-resnets-by","slug":"res-se-net-boosting-performance-of-resnets-by","title":"RES-SE-NET: Boosting Performance of Resnets by Enhancing Bridge-connections","date":"2019-02-16","arxiv_id":"1902.06066","n_code_links":5,"syntology":null},{"paper":null,"slug":"3d-graph-embedding-learning-with-a-structure","title":"3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation","date":"2019-02-14","arxiv_id":"1902.05247","n_code_links":0,"syntology":null},{"paper":"/paper/multigrain-a-unified-image-embedding-for","slug":"multigrain-a-unified-image-embedding-for","title":"MultiGrain: a unified image embedding for classes and instances","date":"2019-02-14","arxiv_id":"1902.05509","n_code_links":3,"syntology":{"ran":4,"of":4,"n_ran_checked":3,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/multigrain"],"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/joint-training-of-neural-network-ensembles","slug":"joint-training-of-neural-network-ensembles","title":"To Ensemble or Not Ensemble: When does End-To-End Training Fail?","date":"2019-02-12","arxiv_id":"1902.04422","n_code_links":1,"syntology":null},{"paper":"/paper/bag-of-freebies-for-training-object-detection","slug":"bag-of-freebies-for-training-object-detection","title":"Bag of Freebies for Training Object Detection Neural Networks","date":"2019-02-11","arxiv_id":"1902.04103","n_code_links":3,"syntology":{"ran":15,"of":18,"n_ran_checked":14,"n_instrument":1,"unverified":3,"pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["dmlc/gluon-cv"],"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/improved-knowledge-distillation-via-teacher","slug":"improved-knowledge-distillation-via-teacher","title":"Improved Knowledge Distillation via Teacher Assistant","date":"2019-02-09","arxiv_id":"1902.03393","n_code_links":3,"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: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["imirzadeh/Teacher-Assistant-Knowledge-Distillation"],"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":null,"slug":"a-single-shot-object-detector-with-feature","title":"A Single-shot Object Detector with Feature Aggragation and Enhancement","date":"2019-02-08","arxiv_id":"1902.02923","n_code_links":0,"syntology":null},{"paper":null,"slug":"software-defined-fpga-accelerator-design-for","title":"Software-Defined FPGA Accelerator Design for Mobile Deep Learning Applications","date":"2019-02-08","arxiv_id":"1902.03192","n_code_links":0,"syntology":null},{"paper":null,"slug":"alphastar-an-evolutionary-computation","title":"AlphaStar: An Evolutionary Computation Perspective","date":"2019-02-05","arxiv_id":"1902.01724","n_code_links":0,"syntology":null},{"paper":"/paper/dvolver-efficient-pareto-optimal-neural","slug":"dvolver-efficient-pareto-optimal-neural","title":"DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search","date":"2019-02-05","arxiv_id":"1902.01654","n_code_links":1,"syntology":null},{"paper":null,"slug":"tzk-flow-based-conditional-generative-model","title":"TzK: Flow-Based Conditional Generative Model","date":"2019-02-05","arxiv_id":"1902.01893","n_code_links":0,"syntology":null},{"paper":"/paper/towards-pedestrian-detection-using-retinanet","slug":"towards-pedestrian-detection-using-retinanet","title":"Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge","date":"2019-02-04","arxiv_id":"1902.01031","n_code_links":1,"syntology":null},{"paper":null,"slug":"micik-mining-cross-layer-inherent-similarity","title":"MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression","date":"2019-02-03","arxiv_id":"1902.00918","n_code_links":0,"syntology":null},{"paper":"/paper/colornet-investigating-the-importance-of","slug":"colornet-investigating-the-importance-of","title":"ColorNet: Investigating the importance of color spaces for image classification","date":"2019-02-01","arxiv_id":"1902.00267","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-hybrid-network-architectures-for","title":"Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge","date":"2019-02-01","arxiv_id":"1902.00460","n_code_links":0,"syntology":null},{"paper":"/paper/learnable-embedding-space-for-efficient","slug":"learnable-embedding-space-for-efficient","title":"Learnable Embedding Space for Efficient Neural Architecture Compression","date":"2019-02-01","arxiv_id":"1902.00383","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":["Friedrich1006/ESNAC"],"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/emerging-convolutions-for-generative","slug":"emerging-convolutions-for-generative","title":"Emerging Convolutions for Generative Normalizing Flows","date":"2019-01-30","arxiv_id":"1901.11137","n_code_links":1,"syntology":null},{"paper":"/paper/real-time-hand-gesture-detection-and","slug":"real-time-hand-gesture-detection-and","title":"Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks","date":"2019-01-29","arxiv_id":"1901.10323","n_code_links":5,"syntology":null},{"paper":null,"slug":"coconet-a-collaborative-convolutional-network","title":"CoCoNet: A Collaborative Convolutional Network","date":"2019-01-28","arxiv_id":"1901.09886","n_code_links":0,"syntology":null},{"paper":null,"slug":"tunet-incorporating-segmentation-maps-to","title":"TUNet: Incorporating segmentation maps to improve classification","date":"2019-01-27","arxiv_id":"1901.11379","n_code_links":0,"syntology":null},{"paper":"/paper/deepsz-a-novel-framework-to-compress-deep","slug":"deepsz-a-novel-framework-to-compress-deep","title":"DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression","date":"2019-01-26","arxiv_id":"1901.09124","n_code_links":1,"syntology":null},{"paper":"/paper/progressive-image-deraining-networks-a-better","slug":"progressive-image-deraining-networks-a-better","title":"Progressive Image Deraining Networks: A Better and Simpler Baseline","date":"2019-01-26","arxiv_id":"1901.09221","n_code_links":4,"syntology":{"ran":9,"of":9,"n_ran_checked":9,"n_instrument":0,"unverified":0,"pointer_only":1,"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) · 0 unverified","official":{"repos":["csdwren/PReNet"],"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/equivariant-transformer-networks","slug":"equivariant-transformer-networks","title":"Equivariant Transformer Networks","date":"2019-01-25","arxiv_id":"1901.11399","n_code_links":3,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["stanford-futuredata/equivariant-transformers"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/revisiting-self-supervised-visual","slug":"revisiting-self-supervised-visual","title":"Revisiting Self-Supervised Visual Representation Learning","date":"2019-01-25","arxiv_id":"1901.09005","n_code_links":6,"syntology":{"ran":9,"of":15,"n_ran_checked":9,"n_instrument":0,"unverified":6,"pointer_only":7,"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) · 6 unverified","official":{"repos":["google/revisiting-self-supervised"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/vision-based-inspection-system-employing","slug":"vision-based-inspection-system-employing","title":"Vision-based inspection system employing computer vision & neural networks for detection of fractures in manufactured components","date":"2019-01-25","arxiv_id":"1901.08864","n_code_links":1,"syntology":null},{"paper":"/paper/in-defense-of-the-triplet-loss-for-visual","slug":"in-defense-of-the-triplet-loss-for-visual","title":"Boosting Standard Classification Architectures Through a Ranking Regularizer","date":"2019-01-24","arxiv_id":"1901.08616","n_code_links":1,"syntology":null},{"paper":"/paper/sequential-skip-prediction-with-few-shot-in","slug":"sequential-skip-prediction-with-few-shot-in","title":"Sequential Skip Prediction with Few-shot in Streamed Music Contents","date":"2019-01-24","arxiv_id":"1901.08203","n_code_links":1,"syntology":null},{"paper":"/paper/traditional-and-heavy-tailed-self","slug":"traditional-and-heavy-tailed-self","title":"Traditional and Heavy-Tailed Self Regularization in Neural Network Models","date":"2019-01-24","arxiv_id":"1901.08276","n_code_links":2,"syntology":{"ran":3,"of":7,"n_ran_checked":3,"n_instrument":0,"unverified":4,"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) · 4 unverified","official":null}},{"paper":"/paper/unsupervised-image-to-image-translation-with-1","slug":"unsupervised-image-to-image-translation-with-1","title":"Unsupervised Image-to-Image Translation with Self-Attention Networks","date":"2019-01-24","arxiv_id":"1901.08242","n_code_links":1,"syntology":null},{"paper":"/paper/bottom-up-object-detection-by-grouping","slug":"bottom-up-object-detection-by-grouping","title":"Bottom-up Object Detection by Grouping Extreme and Center Points","date":"2019-01-23","arxiv_id":"1901.08043","n_code_links":2,"syntology":{"ran":1,"of":5,"n_ran_checked":1,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"1 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; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["xingyizhou/ExtremeNet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/towards-compact-convnets-via-structure","slug":"towards-compact-convnets-via-structure","title":"Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning","date":"2019-01-23","arxiv_id":"1901.07827","n_code_links":1,"syntology":null},{"paper":"/paper/hybrid-task-cascade-for-instance-segmentation","slug":"hybrid-task-cascade-for-instance-segmentation","title":"Hybrid Task Cascade for Instance Segmentation","date":"2019-01-22","arxiv_id":"1901.07518","n_code_links":5,"syntology":{"ran":11,"of":11,"n_ran_checked":11,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["open-mmlab/mmdetection"],"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":"accumulation-bit-width-scaling-for-ultra-low","title":"Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks","date":"2019-01-19","arxiv_id":"1901.06588","n_code_links":0,"syntology":null},{"paper":null,"slug":"consistent-optimization-for-single-shot","title":"Consistent Optimization for Single-Shot Object Detection","date":"2019-01-19","arxiv_id":"1901.06563","n_code_links":0,"syntology":null},{"paper":"/paper/domain-adaptation-for-structured-output-via","slug":"domain-adaptation-for-structured-output-via","title":"Domain Adaptation for Structured Output via Discriminative Patch Representations","date":"2019-01-16","arxiv_id":"1901.05427","n_code_links":8,"syntology":{"ran":4,"of":4,"n_ran_checked":2,"n_instrument":2,"unverified":0,"pointer_only":4,"phrase":"4 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["wasidennis/AdaptSegNet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/upsnet-a-unified-panoptic-segmentation","slug":"upsnet-a-unified-panoptic-segmentation","title":"UPSNet: A Unified Panoptic Segmentation Network","date":"2019-01-12","arxiv_id":"1901.03784","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["uber-research/UPSNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/fishnet-a-versatile-backbone-for-image-region","slug":"fishnet-a-versatile-backbone-for-image-region","title":"FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction","date":"2019-01-11","arxiv_id":"1901.03495","n_code_links":6,"syntology":null},{"paper":null,"slug":"low-precision-constant-parameter-cnn-on-fpga","title":"Low Precision Constant Parameter CNN on FPGA","date":"2019-01-11","arxiv_id":"1901.04969","n_code_links":0,"syntology":null},{"paper":"/paper/region-proposal-by-guided-anchoring","slug":"region-proposal-by-guided-anchoring","title":"Region Proposal by Guided Anchoring","date":"2019-01-10","arxiv_id":"1901.03278","n_code_links":2,"syntology":null},{"paper":"/paper/retinamask-learning-to-predict-masks-improves","slug":"retinamask-learning-to-predict-masks-improves","title":"RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free","date":"2019-01-10","arxiv_id":"1901.03353","n_code_links":53,"syntology":null},{"paper":null,"slug":"collaborative-execution-of-deep-neural","title":"Collaborative Execution of Deep Neural Networks on Internet of Things Devices","date":"2019-01-08","arxiv_id":"1901.02537","n_code_links":0,"syntology":null},{"paper":"/paper/guidelines-and-benchmarks-for-deployment-of","slug":"guidelines-and-benchmarks-for-deployment-of","title":"Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps","date":"2019-01-08","arxiv_id":"1901.02144","n_code_links":1,"syntology":null},{"paper":"/paper/panoptic-feature-pyramid-networks","slug":"panoptic-feature-pyramid-networks","title":"Panoptic Feature Pyramid Networks","date":"2019-01-08","arxiv_id":"1901.02446","n_code_links":12,"syntology":{"ran":11,"of":12,"n_ran_checked":4,"n_instrument":7,"unverified":1,"pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 7 where Syntology's instrument failed) · 1 unverified","official":{"repos":["facebookresearch/detectron2"],"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/deeper-and-wider-siamese-networks-for-real","slug":"deeper-and-wider-siamese-networks-for-real","title":"Deeper and Wider Siamese Networks for Real-Time Visual Tracking","date":"2019-01-07","arxiv_id":"1901.01660","n_code_links":5,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"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) · 0 unverified","official":{"repos":["researchmm/SiamDW"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/dsconv-efficient-convolution-operator","slug":"dsconv-efficient-convolution-operator","title":"DSConv: Efficient Convolution Operator","date":"2019-01-07","arxiv_id":"1901.01928","n_code_links":1,"syntology":null},{"paper":"/paper/scale-aware-trident-networks-for-object","slug":"scale-aware-trident-networks-for-object","title":"Scale-Aware Trident Networks for Object Detection","date":"2019-01-07","arxiv_id":"1901.01892","n_code_links":4,"syntology":null},{"paper":null,"slug":"bandwidth-reduction-using-importance-weighted","title":"Bandwidth Reduction using Importance Weighted Pruning on Ring AllReduce","date":"2019-01-06","arxiv_id":"1901.01544","n_code_links":0,"syntology":null},{"paper":"/paper/a-performance-comparison-of-loss-functions","slug":"a-performance-comparison-of-loss-functions","title":"A Performance Comparison of Loss Functions for Deep Face Recognition","date":"2019-01-01","arxiv_id":"1901.05903","n_code_links":1,"syntology":null},{"paper":null,"slug":"handwritten-indic-character-recognition-using","title":"Handwritten Indic Character Recognition using Capsule Networks","date":"2019-01-01","arxiv_id":"1901.00166","n_code_links":0,"syntology":null},{"paper":"/paper/admm-nn-an-algorithm-hardware-co-design","slug":"admm-nn-an-algorithm-hardware-co-design","title":"ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers","date":"2018-12-31","arxiv_id":"1812.11677","n_code_links":1,"syntology":null},{"paper":null,"slug":"camloc-pedestrian-location-detection-from","title":"CamLoc: Pedestrian Location Detection from Pose Estimation on Resource-constrained Smart-cameras","date":"2018-12-28","arxiv_id":"1812.11209","n_code_links":0,"syntology":null},{"paper":"/paper/car-detection-using-unmanned-aerial-vehicles","slug":"car-detection-using-unmanned-aerial-vehicles","title":"Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3","date":"2018-12-28","arxiv_id":"1812.10968","n_code_links":1,"syntology":null},{"paper":"/paper/dynamic-runtime-feature-map-pruning","slug":"dynamic-runtime-feature-map-pruning","title":"Dynamic Runtime Feature Map Pruning","date":"2018-12-24","arxiv_id":"1812.09922","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-optical-frontend-for-a-convolutional","title":"An Optical Frontend for a Convolutional Neural Network","date":"2018-12-23","arxiv_id":"1901.03661","n_code_links":0,"syntology":null},{"paper":"/paper/chamnet-towards-efficient-network-design","slug":"chamnet-towards-efficient-network-design","title":"ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation","date":"2018-12-21","arxiv_id":"1812.08934","n_code_links":1,"syntology":null},{"paper":"/paper/slimmable-neural-networks","slug":"slimmable-neural-networks","title":"Slimmable Neural Networks","date":"2018-12-21","arxiv_id":"1812.08928","n_code_links":4,"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":["JiahuiYu/slimmable_networks"],"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":"group-attention-single-shot-detector-ga-ssd","title":"Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images","date":"2018-12-18","arxiv_id":"1812.07166","n_code_links":0,"syntology":null},{"paper":"/paper/trust-region-based-adversarial-attack-on","slug":"trust-region-based-adversarial-attack-on","title":"Trust Region Based Adversarial Attack on Neural Networks","date":"2018-12-16","arxiv_id":"1812.06371","n_code_links":2,"syntology":{"ran":7,"of":9,"n_ran_checked":0,"n_instrument":7,"unverified":2,"pointer_only":7,"phrase":"7 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; 7 where Syntology's instrument failed) · 2 unverified","official":{"repos":["amirgholami/trattack"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"a-low-effort-approach-to-structured-cnn","title":"A Low Effort Approach to Structured CNN Design Using PCA","date":"2018-12-15","arxiv_id":"1812.06224","n_code_links":0,"syntology":null},{"paper":"/paper/deepcalib-a-deep-learning-approach-for","slug":"deepcalib-a-deep-learning-approach-for","title":"DeepCalib: a deep learning approach for automatic intrinsic calibration of wide field-of-view cameras","date":"2018-12-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/elastic-improving-cnns-with-instance-specific","slug":"elastic-improving-cnns-with-instance-specific","title":"ELASTIC: Improving CNNs with Dynamic Scaling Policies","date":"2018-12-13","arxiv_id":"1812.05262","n_code_links":1,"syntology":{"ran":3,"of":10,"n_ran_checked":2,"n_instrument":1,"unverified":7,"pointer_only":1,"phrase":"3 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; 1 where Syntology's instrument failed) · 7 unverified","official":{"repos":["allenai/elastic"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/irlas-inverse-reinforcement-learning-for","slug":"irlas-inverse-reinforcement-learning-for","title":"IRLAS: Inverse Reinforcement Learning for Architecture Search","date":"2018-12-13","arxiv_id":"1812.05285","n_code_links":1,"syntology":null},{"paper":"/paper/concentrated-comprehensive-convolutions-for","slug":"concentrated-comprehensive-convolutions-for","title":"C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation","date":"2018-12-12","arxiv_id":"1812.04920","n_code_links":2,"syntology":null},{"paper":"/paper/deep-anomaly-detection-with-outlier-exposure","slug":"deep-anomaly-detection-with-outlier-exposure","title":"Deep Anomaly Detection with Outlier Exposure","date":"2018-12-11","arxiv_id":"1812.04606","n_code_links":9,"syntology":{"ran":5,"of":5,"n_ran_checked":3,"n_instrument":2,"unverified":0,"pointer_only":4,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["hendrycks/outlier-exposure"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/deep-networks-with-probabilistic-gates","slug":"deep-networks-with-probabilistic-gates","title":"Channel selection using Gumbel Softmax","date":"2018-12-11","arxiv_id":"1812.04180","n_code_links":1,"syntology":null},{"paper":"/paper/layer-parallel-training-of-deep-residual","slug":"layer-parallel-training-of-deep-residual","title":"Layer-Parallel Training of Deep Residual Neural Networks","date":"2018-12-11","arxiv_id":"1812.04352","n_code_links":1,"syntology":null},{"paper":null,"slug":"accelerating-convolutional-neural-networks","title":"Accelerating Convolutional Neural Networks via Activation Map Compression","date":"2018-12-10","arxiv_id":"1812.04056","n_code_links":0,"syntology":null},{"paper":"/paper/attention-guided-unified-network-for-panoptic","slug":"attention-guided-unified-network-for-panoptic","title":"Attention-guided Unified Network for Panoptic Segmentation","date":"2018-12-10","arxiv_id":"1812.03904","n_code_links":0,"syntology":null},{"paper":"/paper/learning-embedding-adaptation-for-few-shot","slug":"learning-embedding-adaptation-for-few-shot","title":"Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions","date":"2018-12-10","arxiv_id":"1812.03664","n_code_links":6,"syntology":null},{"paper":"/paper/the-effects-of-super-resolution-on-object","slug":"the-effects-of-super-resolution-on-object","title":"The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery","date":"2018-12-10","arxiv_id":"1812.04098","n_code_links":2,"syntology":null},{"paper":null,"slug":"a-comparison-of-embedded-deep-learning","title":"A Comparison of Embedded Deep Learning Methods for Person Detection","date":"2018-12-09","arxiv_id":"1812.03451","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-structured-model-for-action-detection","title":"A Structured Model For Action Detection","date":"2018-12-09","arxiv_id":"1812.03544","n_code_links":0,"syntology":null},{"paper":"/paper/fbnet-hardware-aware-efficient-convnet-design","slug":"fbnet-hardware-aware-efficient-convnet-design","title":"FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search","date":"2018-12-09","arxiv_id":"1812.03443","n_code_links":5,"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":["facebookresearch/mobile-vision"],"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/spatial-temporal-person-re-identification","slug":"spatial-temporal-person-re-identification","title":"Spatial-Temporal Person Re-identification","date":"2018-12-08","arxiv_id":"1812.03282","n_code_links":3,"syntology":{"ran":8,"of":10,"n_ran_checked":8,"n_instrument":0,"unverified":2,"pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["Wanggcong/Spatial-Temporal-Re-identification"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/variational-saccading-efficient-inference-for","slug":"variational-saccading-efficient-inference-for","title":"Variational Saccading: Efficient Inference for Large Resolution Images","date":"2018-12-08","arxiv_id":"1812.03170","n_code_links":1,"syntology":null},{"paper":"/paper/dosed-a-deep-learning-approach-to-detect","slug":"dosed-a-deep-learning-approach-to-detect","title":"DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal","date":"2018-12-07","arxiv_id":"1812.04079","n_code_links":1,"syntology":null},{"paper":null,"slug":"optimizing-speedaccuracy-trade-off-for-person","title":"Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation","date":"2018-12-07","arxiv_id":"1812.02937","n_code_links":0,"syntology":null},{"paper":null,"slug":"shufflenasnets-efficient-cnn-models-through","title":"ShuffleNASNets: Efficient CNN models through modified Efficient Neural Architecture Search","date":"2018-12-07","arxiv_id":"1812.02975","n_code_links":0,"syntology":null},{"paper":null,"slug":"dsnet-for-real-time-driving-scene-semantic","title":"DSNet for Real-Time Driving Scene Semantic Segmentation","date":"2018-12-06","arxiv_id":"1812.07049","n_code_links":0,"syntology":null},{"paper":"/paper/knowledge-distillation-from-few-samples","slug":"knowledge-distillation-from-few-samples","title":"Few Sample Knowledge Distillation for Efficient Network Compression","date":"2018-12-05","arxiv_id":"1812.01839","n_code_links":1,"syntology":null},{"paper":"/paper/autofocus-efficient-multi-scale-inference","slug":"autofocus-efficient-multi-scale-inference","title":"AutoFocus: Efficient Multi-Scale Inference","date":"2018-12-04","arxiv_id":"1812.01600","n_code_links":1,"syntology":null},{"paper":"/paper/bag-of-tricks-for-image-classification-with","slug":"bag-of-tricks-for-image-classification-with","title":"Bag of Tricks for Image Classification with Convolutional Neural Networks","date":"2018-12-04","arxiv_id":"1812.01187","n_code_links":28,"syntology":{"ran":11,"of":15,"n_ran_checked":9,"n_instrument":2,"unverified":4,"pointer_only":5,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["dmlc/gluon-cv"],"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":"channel-wise-pruning-of-neural-networks-with","title":"Channel-wise pruning of neural networks with tapering resource constraint","date":"2018-12-04","arxiv_id":"1812.07060","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-fuse-things-and-stuff","slug":"learning-to-fuse-things-and-stuff","title":"Learning to Fuse Things and Stuff","date":"2018-12-04","arxiv_id":"1812.01192","n_code_links":0,"syntology":null},{"paper":null,"slug":"identification-and-recognition-of-rice","title":"Identification and Recognition of Rice Diseases and Pests Using Convolutional Neural Networks","date":"2018-12-03","arxiv_id":"1812.01043","n_code_links":0,"syntology":null},{"paper":"/paper/proxylessnas-direct-neural-architecture","slug":"proxylessnas-direct-neural-architecture","title":"ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware","date":"2018-12-02","arxiv_id":"1812.00332","n_code_links":23,"syntology":{"ran":17,"of":27,"n_ran_checked":14,"n_instrument":3,"unverified":10,"pointer_only":4,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 2 honoured, 0 violated, 12 with no contract checked; 3 where Syntology's instrument failed) · 10 unverified","official":{"repos":["MIT-HAN-LAB/ProxylessNAS"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/can-we-gain-more-from-orthogonality-1","slug":"can-we-gain-more-from-orthogonality-1","title":"Can We Gain More from Orthogonality Regularizations in Training Deep Networks?","date":"2018-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"kalman-normalization-normalizing-internal","title":"Kalman Normalization: Normalizing Internal Representations Across Network Layers","date":"2018-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/pelee-a-real-time-object-detection-system-on-1","slug":"pelee-a-real-time-object-detection-system-on-1","title":"Pelee: A Real-Time Object Detection System on Mobile Devices","date":"2018-12-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/revisiting-multi-task-learning-with-rock-a","slug":"revisiting-multi-task-learning-with-rock-a","title":"Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection","date":"2018-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/symbolic-graph-reasoning-meets-convolutions","slug":"symbolic-graph-reasoning-meets-convolutions","title":"Symbolic Graph Reasoning Meets Convolutions","date":"2018-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/graph-based-global-reasoning-networks","slug":"graph-based-global-reasoning-networks","title":"Graph-Based Global Reasoning Networks","date":"2018-11-30","arxiv_id":"1811.12814","n_code_links":9,"syntology":{"ran":15,"of":15,"n_ran_checked":10,"n_instrument":5,"unverified":0,"pointer_only":7,"phrase":"15 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; 5 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/GloRe"],"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":"/paper/making-classification-competitive-for-deep","slug":"making-classification-competitive-for-deep","title":"Classification is a Strong Baseline for Deep Metric Learning","date":"2018-11-30","arxiv_id":"1811.12649","n_code_links":2,"syntology":{"ran":1,"of":5,"n_ran_checked":1,"n_instrument":0,"unverified":4,"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) · 4 unverified","official":{"repos":["azgo14/classification_metric_learning"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/parsing-r-cnn-for-instance-level-human","slug":"parsing-r-cnn-for-instance-level-human","title":"Parsing R-CNN for Instance-Level Human Analysis","date":"2018-11-30","arxiv_id":"1811.12596","n_code_links":2,"syntology":{"ran":6,"of":7,"n_ran_checked":5,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["soeaver/Parsing-R-CNN"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/transferable-adversarial-attacks-for-image","slug":"transferable-adversarial-attacks-for-image","title":"Transferable Adversarial Attacks for Image and Video Object Detection","date":"2018-11-30","arxiv_id":"1811.12641","n_code_links":3,"syntology":{"ran":4,"of":5,"n_ran_checked":1,"n_instrument":3,"unverified":1,"pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"effective-fast-and-memory-efficient","title":"Effective, Fast, and Memory-Efficient Compressed Multi-function Convolutional Neural Networks for More Accurate Medical Image Classification","date":"2018-11-29","arxiv_id":"1811.11996","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-semantic-segmentation-for-visual","title":"Efficient Semantic Segmentation for Visual Bird's-eye View Interpretation","date":"2018-11-29","arxiv_id":"1811.12008","n_code_links":0,"syntology":null},{"paper":"/paper/grid-r-cnn","slug":"grid-r-cnn","title":"Grid R-CNN","date":"2018-11-29","arxiv_id":"1811.12030","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/imagenet-trained-cnns-are-biased-towards","slug":"imagenet-trained-cnns-are-biased-towards","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","date":"2018-11-29","arxiv_id":"1811.12231","n_code_links":7,"syntology":{"ran":3,"of":6,"n_ran_checked":3,"n_instrument":0,"unverified":3,"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) · 3 unverified","official":{"repos":["rgeirhos/Stylized-ImageNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}}],"record_sha256":"b4afd32045d026cb268a6ed3b55930255109393e512bf964c100f8776b07f2e3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}