{"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/85","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":85,"pages_in_order":196,"rows_per_page":100,"rows":[8401,8500],"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/84","next":"/method/convolution/papers/86","papers":[{"paper":null,"slug":"an-efficient-polyp-segmentation-network","title":"An Efficient Polyp Segmentation Network","date":"2022-03-08","arxiv_id":"2203.04118","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-based-lip-audio-visual-synthesis","title":"Attention-Based Lip Audio-Visual Synthesis for Talking Face Generation in the Wild","date":"2022-03-08","arxiv_id":"2203.03984","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-mask-r-cnn-performance-for-long-thin","title":"Boosting Mask R-CNN Performance for Long, Thin Forensic Traces with Pre-Segmentation and IoU Region Merging","date":"2022-03-08","arxiv_id":"2203.03886","n_code_links":0,"syntology":null},{"paper":"/paper/counting-with-adaptive-auxiliary-learning","slug":"counting-with-adaptive-auxiliary-learning","title":"Counting with Adaptive Auxiliary Learning","date":"2022-03-08","arxiv_id":"2203.04061","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-multi-branch-aggregation-network-for","title":"Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes","date":"2022-03-08","arxiv_id":"2203.04037","n_code_links":0,"syntology":null},{"paper":"/paper/edgeformer-improving-light-weight-convnets-by","slug":"edgeformer-improving-light-weight-convnets-by","title":"ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer","date":"2022-03-08","arxiv_id":"2203.03952","n_code_links":3,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":5,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["hkzhang91/edgeformer","hkzhang91/pacc-net","hkzhang91/parc-net"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/gaitstrip-gait-recognition-via-effective","slug":"gaitstrip-gait-recognition-via-effective","title":"GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework","date":"2022-03-08","arxiv_id":"2203.03966","n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-reinforcement-learning-for-predictive","title":"Designing Heterogeneous GNNs with Desired Permutation Properties for Wireless Resource Allocation","date":"2022-03-08","arxiv_id":"2203.03906","n_code_links":0,"syntology":null},{"paper":null,"slug":"semantic-distillation-guided-salient-object","title":"Semantic Distillation Guided Salient Object Detection","date":"2022-03-08","arxiv_id":"2203.04076","n_code_links":0,"syntology":null},{"paper":"/paper/styleheat-one-shot-high-resolution-editable","slug":"styleheat-one-shot-high-resolution-editable","title":"StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN","date":"2022-03-08","arxiv_id":"2203.04036","n_code_links":1,"syntology":{"ran":4,"of":8,"n_ran_checked":4,"n_instrument":0,"unverified":4,"pointer_only":1,"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) · 4 unverified","official":{"repos":["FeiiYin/StyleHEAT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"efficient-cooperation-strategy-generation-in","title":"Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network","date":"2022-03-07","arxiv_id":"2203.03265","n_code_links":0,"syntology":null},{"paper":"/paper/glidenet-global-local-and-intrinsic-based","slug":"glidenet-global-local-and-intrinsic-based","title":"GlideNet: Global, Local and Intrinsic based Dense Embedding NETwork for Multi-category Attributes Prediction","date":"2022-03-07","arxiv_id":"2203.03079","n_code_links":1,"syntology":null},{"paper":"/paper/l2cs-net-fine-grained-gaze-estimation-in","slug":"l2cs-net-fine-grained-gaze-estimation-in","title":"L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments","date":"2022-03-07","arxiv_id":"2203.03339","n_code_links":2,"syntology":{"ran":8,"of":10,"n_ran_checked":5,"n_instrument":3,"unverified":2,"pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"p2m-a-processing-in-pixel-in-memory-paradigm","title":"P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications","date":"2022-03-07","arxiv_id":"2203.04737","n_code_links":0,"syntology":null},{"paper":"/paper/s-rocket-selective-random-convolution-kernels","slug":"s-rocket-selective-random-convolution-kernels","title":"S-Rocket: Selective Random Convolution Kernels for Time Series Classification","date":"2022-03-07","arxiv_id":"2203.03445","n_code_links":1,"syntology":null},{"paper":null,"slug":"singular-value-perturbation-and-deep-network","title":"Singular Value Perturbation and Deep Network Optimization","date":"2022-03-07","arxiv_id":"2203.03099","n_code_links":0,"syntology":null},{"paper":"/paper/tensor-programs-v-tuning-large-neural","slug":"tensor-programs-v-tuning-large-neural","title":"Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer","date":"2022-03-07","arxiv_id":"2203.03466","n_code_links":7,"syntology":{"ran":3,"of":5,"n_ran_checked":1,"n_instrument":2,"unverified":2,"pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["microsoft/mup"],"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":"unsupervised-domain-adaptation-with-9","title":"Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation","date":"2022-03-07","arxiv_id":"2203.03664","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-reinforcement-learning-based-model-free","title":"Deep Reinforcement Learning based Model-free On-line Dynamic Multi-Microgrid Formation to Enhance Resilience","date":"2022-03-06","arxiv_id":"2203.03030","n_code_links":0,"syntology":null},{"paper":null,"slug":"depthwise-convolution-for-multi-agent","title":"Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation","date":"2022-03-06","arxiv_id":"2203.02896","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-optical-flow-guided-motion-and","title":"Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding","date":"2022-03-06","arxiv_id":"2203.02966","n_code_links":0,"syntology":null},{"paper":"/paper/boosting-crowd-counting-via-multifaceted","slug":"boosting-crowd-counting-via-multifaceted","title":"Boosting Crowd Counting via Multifaceted Attention","date":"2022-03-05","arxiv_id":"2203.02636","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":{"repos":["loralinh/boosting-crowd-counting-via-multifaceted-attention"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"don-t-be-so-dense-sparse-to-sparse-gan","title":"Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance","date":"2022-03-05","arxiv_id":"2203.02770","n_code_links":0,"syntology":null},{"paper":null,"slug":"drawinginstyles-portrait-image-generation-and","title":"DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN","date":"2022-03-05","arxiv_id":"2203.02762","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploration-of-various-deep-learning-models","title":"Exploration of Various Deep Learning Models for Increased Accuracy in Automatic Polyp Detection","date":"2022-03-04","arxiv_id":"2203.04093","n_code_links":0,"syntology":null},{"paper":null,"slug":"plant-species-recognition-with-optimized-3d","title":"Plant Species Recognition with Optimized 3D Polynomial Neural Networks and Variably Overlapping Time-Coherent Sliding Window","date":"2022-03-04","arxiv_id":"2203.02611","n_code_links":0,"syntology":null},{"paper":"/paper/pseudo-stereo-for-monocular-3d-object","slug":"pseudo-stereo-for-monocular-3d-object","title":"Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving","date":"2022-03-04","arxiv_id":"2203.02112","n_code_links":1,"syntology":null},{"paper":null,"slug":"quantum-deep-learning-for-mutant-covid-19","title":"Quantum Deep Learning for Mutant COVID-19 Strain Prediction","date":"2022-03-04","arxiv_id":"2203.03556","n_code_links":0,"syntology":null},{"paper":"/paper/sfpn-synthetic-fpn-for-object-detection","slug":"sfpn-synthetic-fpn-for-object-detection","title":"SFPN: Synthetic FPN for Object Detection","date":"2022-03-04","arxiv_id":"2203.02445","n_code_links":1,"syntology":null},{"paper":null,"slug":"structured-pruning-is-all-you-need-for","title":"Structured Pruning is All You Need for Pruning CNNs at Initialization","date":"2022-03-04","arxiv_id":"2203.02549","n_code_links":0,"syntology":null},{"paper":"/paper/uvcgan-unet-vision-transformer-cycle","slug":"uvcgan-unet-vision-transformer-cycle","title":"UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation","date":"2022-03-04","arxiv_id":"2203.02557","n_code_links":2,"syntology":null},{"paper":null,"slug":"virtual-histological-staining-of-label-free","title":"Virtual Histological Staining of Label-Free Total Absorption Photoacoustic Remote Sensing (TA-PARS)","date":"2022-03-04","arxiv_id":"2203.02584","n_code_links":0,"syntology":null},{"paper":"/paper/ad-corre-adaptive-correlation-based-loss-for","slug":"ad-corre-adaptive-correlation-based-loss-for","title":"Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild","date":"2022-03-03","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/generative-modeling-for-low-dimensional","slug":"generative-modeling-for-low-dimensional","title":"Generative Modeling for Low Dimensional Speech Attributes with Neural Spline Flows","date":"2022-03-03","arxiv_id":"2203.01786","n_code_links":1,"syntology":null},{"paper":"/paper/lgt-net-indoor-panoramic-room-layout","slug":"lgt-net-indoor-panoramic-room-layout","title":"LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network","date":"2022-03-03","arxiv_id":"2203.01824","n_code_links":1,"syntology":{"ran":29,"of":41,"n_ran_checked":20,"n_instrument":9,"unverified":12,"pointer_only":0,"phrase":"29 ran (of which 5 constructed an object rather than computing a result; 20 with no instrument failure: 2 honoured, 1 violated, 17 with no contract checked; 9 where Syntology's instrument failed) · 12 unverified","official":{"repos":["zhigangjiang/LGT-Net"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":9,"ran_from_kinds":["found_in_text","official"]}}},{"paper":null,"slug":"modality-adaptive-mixup-and-invariant","title":"Modality-Adaptive Mixup and Invariant Decomposition for RGB-Infrared Person Re-Identification","date":"2022-03-03","arxiv_id":"2203.01735","n_code_links":0,"syntology":null},{"paper":"/paper/polarity-sampling-quality-and-diversity","slug":"polarity-sampling-quality-and-diversity","title":"Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values","date":"2022-03-03","arxiv_id":"2203.01993","n_code_links":1,"syntology":null},{"paper":"/paper/selective-residual-m-net-for-real-image","slug":"selective-residual-m-net-for-real-image","title":"Selective Residual M-Net for Real Image Denoising","date":"2022-03-03","arxiv_id":"2203.01645","n_code_links":1,"syntology":null},{"paper":"/paper/tctrack-temporal-contexts-for-aerial-tracking","slug":"tctrack-temporal-contexts-for-aerial-tracking","title":"TCTrack: Temporal Contexts for Aerial Tracking","date":"2022-03-03","arxiv_id":"2203.01885","n_code_links":1,"syntology":null},{"paper":null,"slug":"understanding-failure-modes-of-self","title":"Measuring Self-Supervised Representation Quality for Downstream Classification using Discriminative Features","date":"2022-03-03","arxiv_id":"2203.01881","n_code_links":0,"syntology":null},{"paper":null,"slug":"vitranspad-video-transformer-using","title":"ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detection","date":"2022-03-03","arxiv_id":"2203.01562","n_code_links":0,"syntology":null},{"paper":"/paper/3dctn-3d-convolution-transformer-network-for","slug":"3dctn-3d-convolution-transformer-network-for","title":"3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification","date":"2022-03-02","arxiv_id":"2203.00828","n_code_links":1,"syntology":null},{"paper":"/paper/advise-adaptive-feature-relevance-and-visual","slug":"advise-adaptive-feature-relevance-and-visual","title":"ADVISE: ADaptive Feature Relevance and VISual Explanations for Convolutional Neural Networks","date":"2022-03-02","arxiv_id":"2203.01289","n_code_links":1,"syntology":null},{"paper":"/paper/contextual-attention-network-transformer","slug":"contextual-attention-network-transformer","title":"Contextual Attention Network: Transformer Meets U-Net","date":"2022-03-02","arxiv_id":"2203.01932","n_code_links":3,"syntology":null},{"paper":null,"slug":"d-2etr-decoder-only-detr-with-computationally","title":"D^2ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale Attention","date":"2022-03-02","arxiv_id":"2203.00860","n_code_links":0,"syntology":null},{"paper":"/paper/dn-detr-accelerate-detr-training-by","slug":"dn-detr-accelerate-detr-training-by","title":"DN-DETR: Accelerate DETR Training by Introducing Query DeNoising","date":"2022-03-02","arxiv_id":"2203.01305","n_code_links":17,"syntology":{"ran":17,"of":22,"n_ran_checked":6,"n_instrument":11,"unverified":5,"pointer_only":9,"phrase":"17 ran (of which 3 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 11 where Syntology's instrument failed) · 5 unverified","official":{"repos":["IDEA-Research/detrex","fengli-ust/dn-detr","idea-research/dn-detr"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"sea-bridging-the-gap-between-one-and-two","title":"SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment","date":"2022-03-02","arxiv_id":"2203.00862","n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-feature-encoding-for-gnns-on-road","title":"Visual Feature Encoding for GNNs on Road Networks","date":"2022-03-02","arxiv_id":"2203.01187","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-neural-ordinary-differential-equation-model","title":"A Neural Ordinary Differential Equation Model for Visualizing Deep Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation","date":"2022-03-01","arxiv_id":"2203.00628","n_code_links":0,"syntology":null},{"paper":"/paper/an-attention-based-u-net-for-detecting","slug":"an-attention-based-u-net-for-detecting","title":"An attention-based U-Net for detecting deforestation within satellite sensor imagery","date":"2022-03-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-depression-detection-via-learning","title":"Automatic Depression Detection via Learning and Fusing Features from Visual Cues","date":"2022-03-01","arxiv_id":"2203.00304","n_code_links":0,"syntology":null},{"paper":null,"slug":"colon-nuclei-instance-segmentation-using-a","title":"Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector","date":"2022-03-01","arxiv_id":"2203.01321","n_code_links":0,"syntology":null},{"paper":null,"slug":"descriptellation-deep-learned-constellation","title":"Descriptellation: Deep Learned Constellation Descriptors","date":"2022-03-01","arxiv_id":"2203.00567","n_code_links":0,"syntology":null},{"paper":"/paper/geobi-gnn-geometry-aware-bi-domain-mesh","slug":"geobi-gnn-geometry-aware-bi-domain-mesh","title":"GeoBi-GNN: Geometry-aware Bi-domain Mesh Denoising via Graph Neural Networks","date":"2022-03-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/omni-frequency-channel-selection","slug":"omni-frequency-channel-selection","title":"Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection","date":"2022-03-01","arxiv_id":"2203.00259","n_code_links":1,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["zhangzjn/ocr-gan"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/real-time-spectrogram-inversion-on-mobile","slug":"real-time-spectrogram-inversion-on-mobile","title":"Real time spectrogram inversion on mobile phone","date":"2022-03-01","arxiv_id":"2203.00756","n_code_links":1,"syntology":null},{"paper":null,"slug":"robots-autonomously-detecting-people-a","title":"Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations","date":"2022-03-01","arxiv_id":"2203.00187","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-deep-learning-powered-ivf-a-large","title":"Towards deep learning-powered IVF: A large public benchmark for morphokinetic parameter prediction","date":"2022-03-01","arxiv_id":"2203.00531","n_code_links":0,"syntology":null},{"paper":"/paper/a-multi-scale-transformer-for-medical-image","slug":"a-multi-scale-transformer-for-medical-image","title":"A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark","date":"2022-02-28","arxiv_id":"2203.00131","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["yhygao/cbim-medical-image-segmentation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/convnext-backbone-hovernet-for-nuclei","slug":"convnext-backbone-hovernet-for-nuclei","title":"ConvNeXt-backbone HoVerNet for nuclei segmentation and classification","date":"2022-02-28","arxiv_id":"2202.13560","n_code_links":1,"syntology":null},{"paper":"/paper/dropit-dropping-intermediate-tensors-for","slug":"dropit-dropping-intermediate-tensors-for","title":"DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training","date":"2022-02-28","arxiv_id":"2202.13808","n_code_links":1,"syntology":null},{"paper":null,"slug":"esw-edge-weights-ensemble-stochastic","title":"ESW Edge-Weights : Ensemble Stochastic Watershed Edge-Weights for Hyperspectral Image Classification","date":"2022-02-28","arxiv_id":"2202.13502","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatiotemporal-transformer-attention-network","title":"Spatiotemporal Transformer Attention Network for 3D Voxel Level Joint Segmentation and Motion Prediction in Point Cloud","date":"2022-02-28","arxiv_id":"2203.00138","n_code_links":0,"syntology":null},{"paper":null,"slug":"state-of-the-art-in-the-architecture-methods","title":"State-of-the-Art in the Architecture, Methods and Applications of StyleGAN","date":"2022-02-28","arxiv_id":"2202.14020","n_code_links":0,"syntology":null},{"paper":"/paper/sunet-swin-transformer-unet-for-image","slug":"sunet-swin-transformer-unet-for-image","title":"SUNet: Swin Transformer UNet for Image Denoising","date":"2022-02-28","arxiv_id":"2202.14009","n_code_links":2,"syntology":null},{"paper":null,"slug":"towards-a-device-independent-deep-learning","title":"Towards A Device-Independent Deep Learning Approach for the Automated Segmentation of Sonographic Fetal Brain Structures: A Multi-Center and Multi-Device Validation","date":"2022-02-28","arxiv_id":"2202.13553","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-multi-scale-swintransformer-htc-with","title":"Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC Challenge","date":"2022-02-28","arxiv_id":"2202.13588","n_code_links":0,"syntology":null},{"paper":"/paper/voxelmorph-going-beyond-the-cranial-vault-1","slug":"voxelmorph-going-beyond-the-cranial-vault-1","title":"Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation","date":"2022-02-28","arxiv_id":"2203.00046","n_code_links":2,"syntology":null},{"paper":null,"slug":"a-computer-vision-assisted-approach-to","title":"A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management","date":"2022-02-27","arxiv_id":"2202.13285","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-dual-neighborhood-hypergraph-neural-network","title":"A Dual Neighborhood Hypergraph Neural Network for Change Detection in VHR Remote Sensing Images","date":"2022-02-27","arxiv_id":"2202.13275","n_code_links":0,"syntology":null},{"paper":null,"slug":"dxm-transfuse-u-net-dual-cross-modal","title":"DXM-TransFuse U-net: Dual Cross-Modal Transformer Fusion U-net for Automated Nerve Identification","date":"2022-02-27","arxiv_id":"2202.13304","n_code_links":0,"syntology":null},{"paper":"/paper/meta-rangeseg-lidar-sequence-semantic","slug":"meta-rangeseg-lidar-sequence-semantic","title":"Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation","date":"2022-02-27","arxiv_id":"2202.13377","n_code_links":1,"syntology":null},{"paper":null,"slug":"analysis-of-visual-reasoning-on-one-stage","title":"Analysis of Visual Reasoning on One-Stage Object Detection","date":"2022-02-26","arxiv_id":"2202.13115","n_code_links":0,"syntology":null},{"paper":"/paper/graph-attention-retrospective","slug":"graph-attention-retrospective","title":"Graph Attention Retrospective","date":"2022-02-26","arxiv_id":"2202.13060","n_code_links":1,"syntology":null},{"paper":null,"slug":"random-access-with-massive-mimo-otfs-in-leo","title":"Random Access with Massive MIMO-OTFS in LEO Satellite Communications","date":"2022-02-26","arxiv_id":"2202.13058","n_code_links":0,"syntology":null},{"paper":"/paper/riconv-effective-rotation-invariant","slug":"riconv-effective-rotation-invariant","title":"RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning","date":"2022-02-26","arxiv_id":"2202.13094","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":1,"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) · 1 unverified","official":{"repos":["cszyzhang/riconv2"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"a-deep-learning-approach-for-network-wide","title":"A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation","date":"2022-02-25","arxiv_id":"2202.12505","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-hardware-aware-system-for-accelerating-deep","title":"A Hardware-Aware System for Accelerating Deep Neural Network Optimization","date":"2022-02-25","arxiv_id":"2202.12954","n_code_links":0,"syntology":null},{"paper":null,"slug":"harmonic-gated-compensation-network-plus-for","title":"Harmonic gated compensation network plus for ICASSP 2022 DNS CHALLENGE","date":"2022-02-25","arxiv_id":"2202.12643","n_code_links":0,"syntology":null},{"paper":null,"slug":"monogenic-wavelet-scattering-network-for","title":"Monogenic Wavelet Scattering Network for Texture Image Classification","date":"2022-02-25","arxiv_id":"2202.12491","n_code_links":0,"syntology":null},{"paper":null,"slug":"novel-techniques-for-improvement-the-nneten","title":"Novel techniques for improving NNetEn entropy calculation for short and noisy time series","date":"2022-02-25","arxiv_id":"2202.12703","n_code_links":0,"syntology":null},{"paper":null,"slug":"structure-aware-unsupervised-tagged-to-cine","title":"Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self Disentanglement","date":"2022-02-25","arxiv_id":"2202.12474","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-adversarial-robustness-from","slug":"understanding-adversarial-robustness-from","title":"Understanding Adversarial Robustness from Feature Maps of Convolutional Layers","date":"2022-02-25","arxiv_id":"2202.12435","n_code_links":1,"syntology":null},{"paper":"/paper/auto-scaling-vision-transformers-without-1","slug":"auto-scaling-vision-transformers-without-1","title":"Auto-scaling Vision Transformers without Training","date":"2022-02-24","arxiv_id":"2202.11921","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":5,"n_instrument":1,"unverified":3,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["vita-group/asvit"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/cg-ssd-corner-guided-single-stage-3d-object","slug":"cg-ssd-corner-guided-single-stage-3d-object","title":"CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point Cloud","date":"2022-02-24","arxiv_id":"2202.11868","n_code_links":1,"syntology":null},{"paper":null,"slug":"controlling-memorability-of-face-images","title":"Controlling Memorability of Face Images","date":"2022-02-24","arxiv_id":"2202.11896","n_code_links":0,"syntology":null},{"paper":"/paper/factorizer-a-scalable-interpretable-approach","slug":"factorizer-a-scalable-interpretable-approach","title":"Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation","date":"2022-02-24","arxiv_id":"2202.12295","n_code_links":2,"syntology":{"ran":9,"of":9,"n_ran_checked":9,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["pashtari/factorizer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"highly-efficient-binary-neural-networks-for","title":"Highly-Efficient Binary Neural Networks for Visual Place Recognition","date":"2022-02-24","arxiv_id":"2202.12375","n_code_links":0,"syntology":null},{"paper":"/paper/improving-robustness-of-convolutional-neural","slug":"improving-robustness-of-convolutional-neural","title":"Improving Robustness of Convolutional Neural Networks Using Element-Wise Activation Scaling","date":"2022-02-24","arxiv_id":"2202.11898","n_code_links":1,"syntology":null},{"paper":"/paper/provable-stochastic-optimization-for-global","slug":"provable-stochastic-optimization-for-global","title":"Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance","date":"2022-02-24","arxiv_id":"2202.12387","n_code_links":1,"syntology":null},{"paper":"/paper/self-distilled-stylegan-towards-generation","slug":"self-distilled-stylegan-towards-generation","title":"Self-Distilled StyleGAN: Towards Generation from Internet Photos","date":"2022-02-24","arxiv_id":"2202.12211","n_code_links":2,"syntology":null},{"paper":"/paper/sonopt-sonifying-bi-objective-population","slug":"sonopt-sonifying-bi-objective-population","title":"SonOpt: Sonifying Bi-objective Population-Based Optimization Algorithms","date":"2022-02-24","arxiv_id":"2202.12187","n_code_links":1,"syntology":null},{"paper":null,"slug":"sutd-prcm-dataset-and-neural-architecture","title":"SUTD-PRCM Dataset and Neural Architecture Search Approach for Complex Metasurface Design","date":"2022-02-24","arxiv_id":"2203.00002","n_code_links":0,"syntology":null},{"paper":null,"slug":"temporal-convolution-domain-adaptation","title":"Temporal Convolution Domain Adaptation Learning for Crops Growth Prediction","date":"2022-02-24","arxiv_id":"2202.12120","n_code_links":0,"syntology":null},{"paper":null,"slug":"unfolding-collective-ais-transmission","title":"Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning","date":"2022-02-24","arxiv_id":"2202.13867","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-bayesian-deep-learning-approach-to-near","title":"A Bayesian Deep Learning Approach to Near-Term Climate Prediction","date":"2022-02-23","arxiv_id":"2202.11244","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-differential-attention-fusion-model-based","title":"A Differential Attention Fusion Model Based on Transformer for Time Series Forecasting","date":"2022-02-23","arxiv_id":"2202.11402","n_code_links":0,"syntology":null},{"paper":"/paper/art-creation-with-multi-conditional-stylegans","slug":"art-creation-with-multi-conditional-stylegans","title":"Art Creation with Multi-Conditional StyleGANs","date":"2022-02-23","arxiv_id":"2202.11777","n_code_links":1,"syntology":null},{"paper":null,"slug":"integration-of-neural-network-and-fuzzy-logic","title":"Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature","date":"2022-02-23","arxiv_id":"2202.12256","n_code_links":0,"syntology":null},{"paper":"/paper/isda-position-aware-instance-segmentation","slug":"isda-position-aware-instance-segmentation","title":"ISDA: Position-Aware Instance Segmentation with Deformable Attention","date":"2022-02-23","arxiv_id":"2202.12251","n_code_links":1,"syntology":null}],"record_sha256":"cfc54925ae833c80a078870ace93cb52b49a18abedaf3a829a7e6a9a7fae7320","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}