{"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/convolution/papers/140","list_of":"/method/convolution","method":"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":140,"pages_in_order":196,"rows_per_page":100,"rows":[13901,14000],"of":19586,"counts":{"archive_papers_tagged":19586,"with_a_code_link":8064,"where_syntology_ran_a_sample":1837,"not_listed_spam_title":0,"listed":19586,"listed_where_code_ran":1837,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1557,"every_run_a_failure_of_syntologys_instrument":280,"listed_with_a_run_with_no_instrument_failure":1557,"listed_every_run_a_failure_of_syntologys_instrument":280,"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/convolution","prev":"/method/convolution/papers/139","next":"/method/convolution/papers/141","papers":[{"paper":null,"slug":"theoretical-insights-into-the-use-of","title":"Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders","date":"2020-04-04","arxiv_id":"2004.01864","n_code_links":0,"syntology":null},{"paper":null,"slug":"volumetric-attention-for-3d-medical-image","title":"Volumetric Attention for 3D Medical Image Segmentation and Detection","date":"2020-04-04","arxiv_id":"2004.01997","n_code_links":0,"syntology":null},{"paper":"/paper/a-fast-fully-octave-convolutional-neural","slug":"a-fast-fully-octave-convolutional-neural","title":"A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation","date":"2020-04-03","arxiv_id":"2004.01317","n_code_links":1,"syntology":null},{"paper":null,"slug":"attribute2vec-deep-network-embedding-through","title":"Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN","date":"2020-04-03","arxiv_id":"2004.01375","n_code_links":0,"syntology":null},{"paper":"/paper/cell-segmentation-and-tracking-using-distance","slug":"cell-segmentation-and-tracking-using-distance","title":"Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy","date":"2020-04-03","arxiv_id":"2004.01486","n_code_links":1,"syntology":null},{"paper":"/paper/context-prior-for-scene-segmentation","slug":"context-prior-for-scene-segmentation","title":"Context Prior for Scene Segmentation","date":"2020-04-03","arxiv_id":"2004.01547","n_code_links":2,"syntology":null},{"paper":"/paper/cppn2gan-combining-compositional-pattern","slug":"cppn2gan-combining-compositional-pattern","title":"CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-scale Pattern Generation","date":"2020-04-03","arxiv_id":"2004.01703","n_code_links":2,"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":["schrum2/GameGAN"],"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":null,"slug":"deep-transfer-learning-for-texture","title":"Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology","date":"2020-04-03","arxiv_id":"2004.01614","n_code_links":0,"syntology":null},{"paper":"/paper/dfnet-discriminative-feature-extraction-and","slug":"dfnet-discriminative-feature-extraction-and","title":"DFNet: Discriminative feature extraction and integration network for salient object detection","date":"2020-04-03","arxiv_id":"2004.01573","n_code_links":1,"syntology":null},{"paper":null,"slug":"effective-fusion-of-deep-multitasking","title":"Effective Fusion of Deep Multitasking Representations for Robust Visual Tracking","date":"2020-04-03","arxiv_id":"2004.01382","n_code_links":0,"syntology":null},{"paper":null,"slug":"feedergan-synthetic-feeder-generation-via","title":"FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets","date":"2020-04-03","arxiv_id":"2004.01407","n_code_links":0,"syntology":null},{"paper":"/paper/google-landmarks-dataset-v2-a-large-scale","slug":"google-landmarks-dataset-v2-a-large-scale","title":"Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval","date":"2020-04-03","arxiv_id":"2004.01804","n_code_links":5,"syntology":null},{"paper":"/paper/spatio-temporal-deformable-convolution-for","slug":"spatio-temporal-deformable-convolution-for","title":"Spatio-temporal deformable convolution for compressed video quality enhancement","date":"2020-04-03","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/spatio-temporal-graph-structure-learning-for","slug":"spatio-temporal-graph-structure-learning-for","title":"Spatio-Temporal Graph Structure Learning for Traffic Forecasting","date":"2020-04-03","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/squeezesegv3-spatially-adaptive-convolution","slug":"squeezesegv3-spatially-adaptive-convolution","title":"SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation","date":"2020-04-03","arxiv_id":"2004.01803","n_code_links":3,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["chenfengxu714/SqueezeSegV3"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/tea-temporal-excitation-and-aggregation-for","slug":"tea-temporal-excitation-and-aggregation-for","title":"TEA: Temporal Excitation and Aggregation for Action Recognition","date":"2020-04-03","arxiv_id":"2004.01398","n_code_links":1,"syntology":null},{"paper":null,"slug":"detection-of-coronavirus-covid-19-associated","title":"Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset","date":"2020-04-02","arxiv_id":"2004.01184","n_code_links":0,"syntology":null},{"paper":"/paper/effect-of-annotation-errors-on-drone","slug":"effect-of-annotation-errors-on-drone","title":"Effect of Annotation Errors on Drone Detection with YOLOv3","date":"2020-04-02","arxiv_id":"2004.01059","n_code_links":1,"syntology":null},{"paper":"/paper/knowing-what-where-and-when-to-look-efficient","slug":"knowing-what-where-and-when-to-look-efficient","title":"Knowing What, Where and When to Look: Efficient Video Action Modeling with Attention","date":"2020-04-02","arxiv_id":"2004.01278","n_code_links":0,"syntology":null},{"paper":null,"slug":"tracking-by-instance-detection-a-meta","title":"Tracking by Instance Detection: A Meta-Learning Approach","date":"2020-04-02","arxiv_id":"2004.00830","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-survey-of-convolutional-neural-networks","title":"A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects","date":"2020-04-01","arxiv_id":"2004.02806","n_code_links":0,"syntology":null},{"paper":null,"slug":"counterfactual-multi-agent-reinforcement","title":"Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication","date":"2020-04-01","arxiv_id":"2004.00470","n_code_links":0,"syntology":null},{"paper":null,"slug":"digit-recognition-using-convolution-neural","title":"Digit Recognition Using Convolution Neural Network","date":"2020-04-01","arxiv_id":"2004.00331","n_code_links":0,"syntology":null},{"paper":"/paper/improved-rawnet-with-filter-wise-rescaling","slug":"improved-rawnet-with-filter-wise-rescaling","title":"Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms","date":"2020-04-01","arxiv_id":"2004.00526","n_code_links":2,"syntology":null},{"paper":null,"slug":"manifold-aware-cyclegan-for-high-resolution","title":"Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis","date":"2020-04-01","arxiv_id":"2004.00173","n_code_links":0,"syntology":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":null,"slug":"attention-based-assisted-excitation-for","title":"Attention-based Assisted Excitation for Salient Object Detection","date":"2020-03-31","arxiv_id":"2003.14194","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-methods-for-detection-and","title":"Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning","date":"2020-03-31","arxiv_id":"2003.14363","n_code_links":0,"syntology":null},{"paper":null,"slug":"characterizing-speech-adversarial-examples","title":"Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement","date":"2020-03-31","arxiv_id":"2003.13917","n_code_links":0,"syntology":null},{"paper":"/paper/covid-resnet-a-deep-learning-framework-for","slug":"covid-resnet-a-deep-learning-framework-for","title":"COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs","date":"2020-03-31","arxiv_id":"2003.14395","n_code_links":1,"syntology":null},{"paper":null,"slug":"diagnosing-covid-19-pneumonia-from-x-ray-and","title":"Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms","date":"2020-03-31","arxiv_id":"2004.00038","n_code_links":0,"syntology":null},{"paper":null,"slug":"fgn-fully-guided-network-for-few-shot","title":"FGN: Fully Guided Network for Few-Shot Instance Segmentation","date":"2020-03-31","arxiv_id":"2003.13954","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-domain-adaptation-for-alignment","title":"Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation","date":"2020-03-31","arxiv_id":"2004.00074","n_code_links":0,"syntology":null},{"paper":"/paper/in-domain-gan-inversion-for-real-image","slug":"in-domain-gan-inversion-for-real-image","title":"In-Domain GAN Inversion for Real Image Editing","date":"2020-03-31","arxiv_id":"2004.00049","n_code_links":2,"syntology":{"ran":5,"of":8,"n_ran_checked":4,"n_instrument":1,"unverified":3,"pointer_only":2,"phrase":"5 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; 1 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":"/paper/interactive-evolution-and-exploration-within","slug":"interactive-evolution-and-exploration-within","title":"Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks","date":"2020-03-31","arxiv_id":"2004.00151","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-oracle-attention-for-high-fidelity","title":"Learning Oracle Attention for High-fidelity Face Completion","date":"2020-03-31","arxiv_id":"2003.13903","n_code_links":0,"syntology":null},{"paper":null,"slug":"radiologist-level-stroke-classification-on","title":"Radiologist-level stroke classification on non-contrast CT scans with Deep U-Net","date":"2020-03-31","arxiv_id":"2003.14287","n_code_links":0,"syntology":null},{"paper":"/paper/self-supervised-monocular-trained-depth","slug":"self-supervised-monocular-trained-depth","title":"Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume","date":"2020-03-31","arxiv_id":"2003.13951","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/spare3d-a-dataset-for-spatial-reasoning-on","slug":"spare3d-a-dataset-for-spatial-reasoning-on","title":"SPARE3D: A Dataset for SPAtial REasoning on Three-View Line Drawings","date":"2020-03-31","arxiv_id":"2003.14034","n_code_links":1,"syntology":null},{"paper":null,"slug":"stylerig-rigging-stylegan-for-3d-control-over","title":"StyleRig: Rigging StyleGAN for 3D Control over Portrait Images","date":"2020-03-31","arxiv_id":"2004.00121","n_code_links":0,"syntology":null},{"paper":"/paper/towards-lifelong-self-supervision-for","slug":"towards-lifelong-self-supervision-for","title":"Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation","date":"2020-03-31","arxiv_id":"2004.00161","n_code_links":1,"syntology":null},{"paper":null,"slug":"actgan-flexible-and-efficient-one-shot-face","title":"ActGAN: Flexible and Efficient One-shot Face Reenactment","date":"2020-03-30","arxiv_id":"2003.13840","n_code_links":0,"syntology":null},{"paper":"/paper/designing-network-design-spaces","slug":"designing-network-design-spaces","title":"Designing Network Design Spaces","date":"2020-03-30","arxiv_id":"2003.13678","n_code_links":26,"syntology":{"ran":48,"of":53,"n_ran_checked":41,"n_instrument":7,"unverified":5,"pointer_only":7,"phrase":"48 ran (of which 0 constructed an object rather than computing a result; 41 with no instrument failure: 4 honoured, 0 violated, 37 with no contract checked; 7 where Syntology's instrument failed) · 5 unverified","official":{"repos":["facebookresearch/pycls"],"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/exploiting-deep-generative-prior-for","slug":"exploiting-deep-generative-prior-for","title":"Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation","date":"2020-03-30","arxiv_id":"2003.13659","n_code_links":1,"syntology":{"ran":14,"of":18,"n_ran_checked":10,"n_instrument":4,"unverified":4,"pointer_only":7,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 1 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","official":{"repos":["XingangPan/deep-generative-prior"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/how-not-to-give-a-flop-combining","slug":"how-not-to-give-a-flop-combining","title":"How Not to Give a FLOP: Combining Regularization and Pruning for Efficient Inference","date":"2020-03-30","arxiv_id":"2003.13593","n_code_links":1,"syntology":null},{"paper":"/paper/l-2-gcn-layer-wise-and-learned-efficient","slug":"l-2-gcn-layer-wise-and-learned-efficient","title":"L^2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks","date":"2020-03-30","arxiv_id":"2003.13606","n_code_links":2,"syntology":null},{"paper":null,"slug":"physical-model-guided-deep-image-deraining","title":"Physical Model Guided Deep Image Deraining","date":"2020-03-30","arxiv_id":"2003.13242","n_code_links":0,"syntology":null},{"paper":"/paper/retinatrack-online-single-stage-joint","slug":"retinatrack-online-single-stage-joint","title":"RetinaTrack: Online Single Stage Joint Detection and Tracking","date":"2020-03-30","arxiv_id":"2003.13870","n_code_links":1,"syntology":null},{"paper":null,"slug":"revisiting-over-smoothing-in-deep-gcns","title":"Revisiting Over-smoothing in Deep GCNs","date":"2020-03-30","arxiv_id":"2003.13663","n_code_links":0,"syntology":null},{"paper":"/paper/tresnet-high-performance-gpu-dedicated","slug":"tresnet-high-performance-gpu-dedicated","title":"TResNet: High Performance GPU-Dedicated Architecture","date":"2020-03-30","arxiv_id":"2003.13630","n_code_links":3,"syntology":null},{"paper":null,"slug":"weakly-supervised-land-classification-for","title":"Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset","date":"2020-03-30","arxiv_id":"2003.13648","n_code_links":0,"syntology":null},{"paper":null,"slug":"defect-segmentation-mapping-tunnel-lining","title":"Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network","date":"2020-03-29","arxiv_id":"2003.13120","n_code_links":0,"syntology":null},{"paper":"/paper/detection-of-3d-bounding-boxes-of-vehicles","slug":"detection-of-3d-bounding-boxes-of-vehicles","title":"Detection of 3D Bounding Boxes of Vehicles Using Perspective Transformation for Accurate Speed Measurement","date":"2020-03-29","arxiv_id":"2003.13137","n_code_links":3,"syntology":null},{"paper":"/paper/high-order-residual-network-for-light-field","slug":"high-order-residual-network-for-light-field","title":"High-Order Residual Network for Light Field Super-Resolution","date":"2020-03-29","arxiv_id":"2003.13094","n_code_links":1,"syntology":{"ran":0,"of":10,"n_ran_checked":0,"n_instrument":0,"unverified":10,"pointer_only":0,"phrase":"0 ran · 10 unverified","official":{"repos":["monaen/LightFieldReconstruction"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":10,"ran_from_kinds":[]}}},{"paper":"/paper/superpixel-segmentation-with-fully","slug":"superpixel-segmentation-with-fully","title":"Superpixel Segmentation with Fully Convolutional Networks","date":"2020-03-29","arxiv_id":"2003.12929","n_code_links":1,"syntology":null},{"paper":null,"slug":"unsupervised-deep-learning-for-mr-angiography","title":"Unsupervised Deep Learning for MR Angiography with Flexible Temporal Resolution","date":"2020-03-29","arxiv_id":"2003.13096","n_code_links":0,"syntology":null},{"paper":"/paper/cakes-channel-wise-automatic-kernel-shrinking","slug":"cakes-channel-wise-automatic-kernel-shrinking","title":"CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks","date":"2020-03-28","arxiv_id":"2003.12798","n_code_links":1,"syntology":null},{"paper":"/paper/cross-domain-detection-via-graph-induced","slug":"cross-domain-detection-via-graph-induced","title":"Cross-domain Detection via Graph-induced Prototype Alignment","date":"2020-03-28","arxiv_id":"2003.12849","n_code_links":1,"syntology":null},{"paper":"/paper/obstacle-avoidance-and-navigation-utilizing","slug":"obstacle-avoidance-and-navigation-utilizing","title":"Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shaping","date":"2020-03-28","arxiv_id":"2003.12863","n_code_links":1,"syntology":null},{"paper":null,"slug":"one-shot-domain-adaptation-for-face","title":"One-Shot Domain Adaptation For Face Generation","date":"2020-03-28","arxiv_id":"2003.12869","n_code_links":0,"syntology":null},{"paper":null,"slug":"algorithm-based-fault-tolerance-for","title":"FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks","date":"2020-03-27","arxiv_id":"2003.12203","n_code_links":0,"syntology":null},{"paper":"/paper/augmenting-colonoscopy-using-extended-and","slug":"augmenting-colonoscopy-using-extended-and","title":"Augmenting Colonoscopy using Extended and Directional CycleGAN for Lossy Image Translation","date":"2020-03-27","arxiv_id":"2003.12473","n_code_links":1,"syntology":null},{"paper":"/paper/controllable-person-image-synthesis-with","slug":"controllable-person-image-synthesis-with","title":"Controllable Person Image Synthesis with Attribute-Decomposed GAN","date":"2020-03-27","arxiv_id":"2003.12267","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 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"deep-cg2real-synthetic-to-real-translation-1","title":"Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement","date":"2020-03-27","arxiv_id":"2003.12649","n_code_links":0,"syntology":null},{"paper":"/paper/dynamic-region-aware-convolution","slug":"dynamic-region-aware-convolution","title":"Dynamic Region-Aware Convolution","date":"2020-03-27","arxiv_id":"2003.12243","n_code_links":0,"syntology":null},{"paper":null,"slug":"imac-in-memory-multi-bit-multiplication","title":"IMAC: In-memory multi-bit Multiplication andACcumulation in 6T SRAM Array","date":"2020-03-27","arxiv_id":"2003.12558","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-evaluation-of-prohibited-item","title":"On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery","date":"2020-03-27","arxiv_id":"2003.12625","n_code_links":0,"syntology":null},{"paper":null,"slug":"one-shot-gan-generated-fake-face-detection","title":"One-Shot GAN Generated Fake Face Detection","date":"2020-03-27","arxiv_id":"2003.12244","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-cross-modal-audio-representation","title":"Unsupervised Cross-Modal Audio Representation Learning from Unstructured Multilingual Text","date":"2020-03-27","arxiv_id":"2003.12265","n_code_links":0,"syntology":null},{"paper":"/paper/bridging-the-gap-between-spectral-and-spatial","slug":"bridging-the-gap-between-spectral-and-spatial","title":"Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks","date":"2020-03-26","arxiv_id":"2003.11702","n_code_links":2,"syntology":null},{"paper":null,"slug":"classification-of-the-chinese-handwritten","title":"Classification of Chinese Handwritten Numbers with Labeled Projective Dictionary Pair Learning","date":"2020-03-26","arxiv_id":"2003.11700","n_code_links":0,"syntology":null},{"paper":"/paper/correspondence-networks-with-adaptive","slug":"correspondence-networks-with-adaptive","title":"Correspondence Networks with Adaptive Neighbourhood Consensus","date":"2020-03-26","arxiv_id":"2003.12059","n_code_links":1,"syntology":{"ran":8,"of":11,"n_ran_checked":7,"n_instrument":1,"unverified":3,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["ActiveVisionLab/ANCNet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/cycle-text-to-image-gan-with-bert","slug":"cycle-text-to-image-gan-with-bert","title":"Cycle Text-To-Image GAN with BERT","date":"2020-03-26","arxiv_id":"2003.12137","n_code_links":4,"syntology":null},{"paper":null,"slug":"fastidious-attention-network-for-navel-orange","title":"Fastidious Attention Network for Navel Orange Segmentation","date":"2020-03-26","arxiv_id":"2003.11734","n_code_links":0,"syntology":null},{"paper":"/paper/hit-detector-hierarchical-trinity","slug":"hit-detector-hierarchical-trinity","title":"Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection","date":"2020-03-26","arxiv_id":"2003.11818","n_code_links":1,"syntology":null},{"paper":"/paper/image-generation-via-minimizing-frechet","slug":"image-generation-via-minimizing-frechet","title":"Image Generation Via Minimizing Fréchet Distance in Discriminator Feature Space","date":"2020-03-26","arxiv_id":"2003.11774","n_code_links":1,"syntology":null},{"paper":"/paper/mask-encoding-for-single-shot-instance","slug":"mask-encoding-for-single-shot-instance","title":"Mask Encoding for Single Shot Instance Segmentation","date":"2020-03-26","arxiv_id":"2003.11712","n_code_links":7,"syntology":null},{"paper":"/paper/memory-enhanced-global-local-aggregation-for","slug":"memory-enhanced-global-local-aggregation-for","title":"Memory Enhanced Global-Local Aggregation for Video Object Detection","date":"2020-03-26","arxiv_id":"2003.12063","n_code_links":2,"syntology":null},{"paper":"/paper/milking-cowmask-for-semi-supervised-image","slug":"milking-cowmask-for-semi-supervised-image","title":"Milking CowMask for Semi-Supervised Image Classification","date":"2020-03-26","arxiv_id":"2003.12022","n_code_links":2,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":0,"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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["google-research/google-research"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-new-multiple-max-pooling-integration-module","title":"A New Multiple Max-pooling Integration Module and Cross Multiscale Deconvolution Network Based on Image Semantic Segmentation","date":"2020-03-25","arxiv_id":"2003.11213","n_code_links":0,"syntology":null},{"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":null,"slug":"dcdlearn-multi-order-deep-cross-distance","title":"DCDLearn: Multi-order Deep Cross-distance Learning for Vehicle Re-Identification","date":"2020-03-25","arxiv_id":"2003.11315","n_code_links":0,"syntology":null},{"paper":null,"slug":"essop-efficient-and-scalable-stochastic-outer","title":"ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning","date":"2020-03-25","arxiv_id":"2003.11256","n_code_links":0,"syntology":null},{"paper":"/paper/greedynas-towards-fast-one-shot-nas-with","slug":"greedynas-towards-fast-one-shot-nas-with","title":"GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet","date":"2020-03-25","arxiv_id":"2003.11236","n_code_links":0,"syntology":null},{"paper":null,"slug":"mim-based-generative-adversarial-networks-and","title":"MIM-Based GAN: Information Metric to Amplify Small Probability Events Importance in Generative Adversarial Networks","date":"2020-03-25","arxiv_id":"2003.11285","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-lead-ecg-classification-via-an","title":"Multi-Lead ECG Classification via an Information-Based Attention Convolutional Neural Network","date":"2020-03-25","arxiv_id":"2003.12009","n_code_links":0,"syntology":null},{"paper":"/paper/pipelined-backpropagation-at-scale-training","slug":"pipelined-backpropagation-at-scale-training","title":"Pipelined Backpropagation at Scale: Training Large Models without Batches","date":"2020-03-25","arxiv_id":"2003.11666","n_code_links":0,"syntology":null},{"paper":null,"slug":"prior-enlightened-and-motion-robust-video","title":"Prior-enlightened and Motion-robust Video Deblurring","date":"2020-03-25","arxiv_id":"2003.11209","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-line-art-video-colorization-with-a-few","title":"Deep Line Art Video Colorization with a Few References","date":"2020-03-24","arxiv_id":"2003.10685","n_code_links":0,"syntology":null},{"paper":"/paper/fadnet-a-fast-and-accurate-network-for","slug":"fadnet-a-fast-and-accurate-network-for","title":"FADNet: A Fast and Accurate Network for Disparity Estimation","date":"2020-03-24","arxiv_id":"2003.10758","n_code_links":2,"syntology":null},{"paper":"/paper/learning-to-reconstruct-confocal-microscopy","slug":"learning-to-reconstruct-confocal-microscopy","title":"Learning to Reconstruct Confocal Microscopy Stacks from Single Light Field Images","date":"2020-03-24","arxiv_id":"2003.11004","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-agent-reinforcement-learning-for-1","title":"Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward","date":"2020-03-24","arxiv_id":"2003.10598","n_code_links":0,"syntology":null},{"paper":null,"slug":"organ-segmentation-from-full-size-ct-images","title":"Organ Segmentation From Full-size CT Images Using Memory-Efficient FCN","date":"2020-03-24","arxiv_id":"2003.10690","n_code_links":0,"syntology":null},{"paper":null,"slug":"re-training-stylegan-a-first-step-towards","title":"Re-Training StyleGAN -- A First Step Towards Building Large, Scalable Synthetic Facial Datasets","date":"2020-03-24","arxiv_id":"2003.10847","n_code_links":0,"syntology":null},{"paper":null,"slug":"real-time-3d-object-proposal-generation-and","title":"Real-time 3D object proposal generation and classification under limited processing resources","date":"2020-03-24","arxiv_id":"2003.10670","n_code_links":0,"syntology":null},{"paper":null,"slug":"tractogram-filtering-of-anatomically-non","title":"Tractogram filtering of anatomically non-plausible fibers with geometric deep learning","date":"2020-03-24","arxiv_id":"2003.11013","n_code_links":0,"syntology":null},{"paper":null,"slug":"training-a-u-net-based-on-a-random-mode","title":"Training a U-Net based on a random mode-coupling matrix model to recover acoustic interference striations","date":"2020-03-24","arxiv_id":"2003.10661","n_code_links":0,"syntology":null},{"paper":null,"slug":"depth-edge-guided-cnns-for-sparse-depth","title":"Depth Edge Guided CNNs for Sparse Depth Upsampling","date":"2020-03-23","arxiv_id":"2003.10138","n_code_links":0,"syntology":null},{"paper":null,"slug":"diagnosis-of-breast-cancer-using-hybrid","title":"Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning","date":"2020-03-23","arxiv_id":"2003.13503","n_code_links":0,"syntology":null},{"paper":null,"slug":"do-recent-advancements-in-model-based-deep-1","title":"Importance of using appropriate baselines for evaluation of data-efficiency in deep reinforcement learning for Atari","date":"2020-03-23","arxiv_id":"2003.10181","n_code_links":0,"syntology":null}],"record_sha256":"63aa26a7f32fc73029f4cd76719269c7f4375d1a82a8996e82483f86c107ca73","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}