{"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/125","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":125,"pages_in_order":196,"rows_per_page":100,"rows":[12401,12500],"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/124","next":"/method/convolution/papers/126","papers":[{"paper":null,"slug":"efficient-grouping-for-keypoint-detection","title":"Efficient grouping for keypoint detection","date":"2020-10-23","arxiv_id":"2010.12390","n_code_links":0,"syntology":null},{"paper":"/paper/ipu-net-multi-scale-identity-preserved-u-net","slug":"ipu-net-multi-scale-identity-preserved-u-net","title":"Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face Recognition","date":"2020-10-23","arxiv_id":"2010.12249","n_code_links":1,"syntology":null},{"paper":"/paper/knowledge-graph-embedding-with-atrous","slug":"knowledge-graph-embedding-with-atrous","title":"Knowledge Graph Embedding with Atrous Convolution and Residual Learning","date":"2020-10-23","arxiv_id":"2010.12121","n_code_links":0,"syntology":null},{"paper":"/paper/kvasir-instrument-diagnostic-and-therapeutic","slug":"kvasir-instrument-diagnostic-and-therapeutic","title":"Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy","date":"2020-10-23","arxiv_id":"2011.08065","n_code_links":1,"syntology":null},{"paper":"/paper/on-the-equivalence-of-decoupled-graph","slug":"on-the-equivalence-of-decoupled-graph","title":"On the Equivalence of Decoupled Graph Convolution Network and Label Propagation","date":"2020-10-23","arxiv_id":"2010.12408","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["DongHande/PT_propagation_then_training"],"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/primal-dual-mesh-convolutional-neural","slug":"primal-dual-mesh-convolutional-neural","title":"Primal-Dual Mesh Convolutional Neural Networks","date":"2020-10-23","arxiv_id":"2010.12455","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"0 ran · 3 unverified","official":{"repos":["MIT-SPARK/PD-MeshNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":"/paper/progressive-training-of-multi-level-wavelet","slug":"progressive-training-of-multi-level-wavelet","title":"Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising","date":"2020-10-23","arxiv_id":"2010.12422","n_code_links":2,"syntology":null},{"paper":null,"slug":"quantum-superposition-spiking-neural-network","title":"Quantum Superposition Inspired Spiking Neural Network","date":"2020-10-23","arxiv_id":"2010.12197","n_code_links":0,"syntology":null},{"paper":"/paper/resnet-or-densenet-introducing-dense","slug":"resnet-or-densenet-introducing-dense","title":"ResNet or DenseNet? Introducing Dense Shortcuts to ResNet","date":"2020-10-23","arxiv_id":"2010.12496","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":3,"phrase":"0 ran · 3 unverified","official":null}},{"paper":null,"slug":"adversarial-attacks-on-deep-algorithmic","title":"Adversarial Attacks on Deep Algorithmic Trading Policies","date":"2020-10-22","arxiv_id":"2010.11388","n_code_links":0,"syntology":null},{"paper":null,"slug":"autopruning-for-deep-neural-network-with","title":"AutoPruning for Deep Neural Network with Dynamic Channel Masking","date":"2020-10-22","arxiv_id":"2010.12021","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-scale-permuted-backbone-with-1","title":"Efficient Scale-Permuted Backbone with Learned Resource Distribution","date":"2020-10-22","arxiv_id":"2010.11426","n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-tuned-pre-trained-mask-r-cnn-models-for","title":"Fine-tuned Pre-trained Mask R-CNN Models for Surface Object Detection","date":"2020-10-22","arxiv_id":"2010.11464","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-occupancy-function-from-point-clouds","title":"Learning Occupancy Function from Point Clouds for Surface Reconstruction","date":"2020-10-22","arxiv_id":"2010.11378","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-sort-image-sequences-via","title":"Learning to Sort Image Sequences via Accumulated Temporal Differences","date":"2020-10-22","arxiv_id":"2010.11649","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-coverage-and-capacity-in-cellular","title":"Optimizing Coverage and Capacity in Cellular Networks using Machine Learning","date":"2020-10-22","arxiv_id":"2010.13710","n_code_links":0,"syntology":null},{"paper":"/paper/phew-paths-with-higher-edge-weights-give-1","slug":"phew-paths-with-higher-edge-weights-give-1","title":"PHEW: Constructing Sparse Networks that Learn Fast and Generalize Well without Training Data","date":"2020-10-22","arxiv_id":"2010.11354","n_code_links":1,"syntology":null},{"paper":null,"slug":"qista-net-dnn-architecture-to-solve-ell-q","title":"QISTA-Net: DNN Architecture to Solve $\\ell_q$-norm Minimization Problem and Image Compressed Sensing","date":"2020-10-22","arxiv_id":"2010.11363","n_code_links":0,"syntology":null},{"paper":null,"slug":"stability-of-algebraic-neural-networks-to","title":"Stability of Algebraic Neural Networks to Small Perturbations","date":"2020-10-22","arxiv_id":"2010.11544","n_code_links":0,"syntology":null},{"paper":null,"slug":"unfolding-neural-networks-for-compressive","title":"Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution","date":"2020-10-22","arxiv_id":"2010.11391","n_code_links":0,"syntology":null},{"paper":null,"slug":"2nd-place-solution-to-instance-segmentation","title":"2nd Place Solution to Instance Segmentation of IJCAI 3D AI Challenge 2020","date":"2020-10-21","arxiv_id":"2010.10957","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-graph-based-and-patient-demographics-aware","title":"A Weighted Heterogeneous Graph Based Dialogue System","date":"2020-10-21","arxiv_id":"2010.10699","n_code_links":0,"syntology":null},{"paper":"/paper/approxdet-content-and-contention-aware","slug":"approxdet-content-and-contention-aware","title":"ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles","date":"2020-10-21","arxiv_id":"2010.10754","n_code_links":1,"syntology":null},{"paper":"/paper/deep-learning-frameworks-for-pavement","slug":"deep-learning-frameworks-for-pavement","title":"Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis","date":"2020-10-21","arxiv_id":"2010.10681","n_code_links":1,"syntology":null},{"paper":null,"slug":"grapheme-or-phoneme-an-analysis-of-tacotron-s","title":"An Investigation of the Relation Between Grapheme Embeddings and Pronunciation for Tacotron-based Systems","date":"2020-10-21","arxiv_id":"2010.10694","n_code_links":0,"syntology":null},{"paper":null,"slug":"importance-aware-semantic-segmentation-in","title":"Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training","date":"2020-10-21","arxiv_id":"2010.12440","n_code_links":0,"syntology":null},{"paper":"/paper/learning-speaker-embedding-from-text-to","slug":"learning-speaker-embedding-from-text-to","title":"Learning Speaker Embedding from Text-to-Speech","date":"2020-10-21","arxiv_id":"2010.11221","n_code_links":1,"syntology":null},{"paper":"/paper/one-model-to-reconstruct-them-all-a-novel-way","slug":"one-model-to-reconstruct-them-all-a-novel-way","title":"One Model to Reconstruct Them All: A Novel Way to Use the Stochastic Noise in StyleGAN","date":"2020-10-21","arxiv_id":"2010.11113","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-graph-learning-for","slug":"self-supervised-graph-learning-for","title":"Self-supervised Graph Learning for Recommendation","date":"2020-10-21","arxiv_id":"2010.10783","n_code_links":3,"syntology":null},{"paper":null,"slug":"self-supervised-wearable-based-activity","title":"Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion","date":"2020-10-21","arxiv_id":"2010.13713","n_code_links":0,"syntology":null},{"paper":"/paper/trajectory-prediction-using-equivariant-1","slug":"trajectory-prediction-using-equivariant-1","title":"Trajectory Prediction using Equivariant Continuous Convolution","date":"2020-10-21","arxiv_id":"2010.11344","n_code_links":0,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":2,"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":null}},{"paper":null,"slug":"transferable-graph-optimizers-for-ml","title":"Transferable Graph Optimizers for ML Compilers","date":"2020-10-21","arxiv_id":"2010.12438","n_code_links":0,"syntology":null},{"paper":null,"slug":"uav-lidar-point-cloud-segmentation-of-a-stack","title":"UAV LiDAR Point Cloud Segmentation of A Stack Interchange with Deep Neural Networks","date":"2020-10-21","arxiv_id":"2010.11106","n_code_links":0,"syntology":null},{"paper":null,"slug":"convolutional-lstm-for-multi-image-to-single","title":"Convolutional-LSTM for Multi-Image to Single Output Medical Prediction","date":"2020-10-20","arxiv_id":"2010.10004","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-overcomplete-representations-for","slug":"exploring-overcomplete-representations-for","title":"Exploring Overcomplete Representations for Single Image Deraining using CNNs","date":"2020-10-20","arxiv_id":"2010.10661","n_code_links":1,"syntology":null},{"paper":"/paper/graph-fairing-convolutional-networks-for","slug":"graph-fairing-convolutional-networks-for","title":"Graph Fairing Convolutional Networks for Anomaly Detection","date":"2020-10-20","arxiv_id":"2010.10274","n_code_links":1,"syntology":null},{"paper":null,"slug":"micro-ct-image-assisted-cross-modality-super","title":"Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset","date":"2020-10-20","arxiv_id":"2010.10207","n_code_links":0,"syntology":null},{"paper":"/paper/small-footprint-keyword-spotting-with-multi","slug":"small-footprint-keyword-spotting-with-multi","title":"Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution","date":"2020-10-20","arxiv_id":"2010.09960","n_code_links":1,"syntology":null},{"paper":"/paper/stronger-faster-and-more-explainable-a-graph","slug":"stronger-faster-and-more-explainable-a-graph","title":"Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition","date":"2020-10-20","arxiv_id":"2010.09978","n_code_links":1,"syntology":null},{"paper":"/paper/the-effect-of-spectrogram-reconstruction-on","slug":"the-effect-of-spectrogram-reconstruction-on","title":"The Effect of Spectrogram Reconstruction on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy","date":"2020-10-20","arxiv_id":"2010.09969","n_code_links":2,"syntology":null},{"paper":null,"slug":"tongji-university-undergraduate-team-for-the","title":"Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020","date":"2020-10-20","arxiv_id":"2010.10145","n_code_links":0,"syntology":null},{"paper":"/paper/towards-maximizing-the-representation-gap","slug":"towards-maximizing-the-representation-gap","title":"Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples","date":"2020-10-20","arxiv_id":"2010.10474","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-backbone-replaceable-fine-tuning-network","title":"A Backbone Replaceable Fine-tuning Framework for Stable Face Alignment","date":"2020-10-19","arxiv_id":"2010.09501","n_code_links":0,"syntology":null},{"paper":"/paper/attention-augmented-convlstm-forenvironment","slug":"attention-augmented-convlstm-forenvironment","title":"Attention Augmented ConvLSTM for Environment Prediction","date":"2020-10-19","arxiv_id":"2010.09662","n_code_links":1,"syntology":null},{"paper":"/paper/bi-real-net-v2-rethinking-non-linearity-for-1-1","slug":"bi-real-net-v2-rethinking-non-linearity-for-1-1","title":"FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going Beyond","date":"2020-10-19","arxiv_id":"2010.09294","n_code_links":1,"syntology":null},{"paper":"/paper/brain-atlas-guided-attention-u-net-for-white","slug":"brain-atlas-guided-attention-u-net-for-white","title":"Brain Atlas Guided Attention U-Net for White Matter Hyperintensity Segmentation","date":"2020-10-19","arxiv_id":"2010.09586","n_code_links":1,"syntology":null},{"paper":null,"slug":"chance-constrained-control-with-lexicographic","title":"Chance-Constrained Control with Lexicographic Deep Reinforcement Learning","date":"2020-10-19","arxiv_id":"2010.09468","n_code_links":0,"syntology":null},{"paper":null,"slug":"circular-convolution-and-product-theorem-for","title":"Circular Convolution and Product Theorem for Affine Discrete Fractional Fourier Transform","date":"2020-10-19","arxiv_id":"2010.09882","n_code_links":0,"syntology":null},{"paper":"/paper/connections-between-relational-event-model","slug":"connections-between-relational-event-model","title":"Connections between Relational Event Model and Inverse Reinforcement Learning for Characterizing Group Interaction Sequences","date":"2020-10-19","arxiv_id":"2010.09810","n_code_links":1,"syntology":null},{"paper":null,"slug":"deepapple-deep-learning-based-apple-detection","title":"DeepApple: Deep Learning-based Apple Detection using a Suppression Mask R-CNN","date":"2020-10-19","arxiv_id":"2010.09870","n_code_links":0,"syntology":null},{"paper":null,"slug":"gans-for-learning-from-very-high-class","title":"GANs for learning from very high class conditional noisy labels","date":"2020-10-19","arxiv_id":"2010.09577","n_code_links":0,"syntology":null},{"paper":null,"slug":"mcgkt-net-multi-level-context-gating","title":"MCGKT-Net: Multi-level Context Gating Knowledge Transfer Network for Single Image Deraining","date":"2020-10-19","arxiv_id":"2010.09241","n_code_links":0,"syntology":null},{"paper":"/paper/mimicnorm-weight-mean-and-last-bn-layer-mimic","slug":"mimicnorm-weight-mean-and-last-bn-layer-mimic","title":"MimicNorm: Weight Mean and Last BN Layer Mimic the Dynamic of Batch Normalization","date":"2020-10-19","arxiv_id":"2010.09278","n_code_links":1,"syntology":null},{"paper":null,"slug":"robustness-aware-2-bit-quantization-with-real","title":"Robustness-aware 2-bit quantization with real-time performance for neural network","date":"2020-10-19","arxiv_id":"2010.11271","n_code_links":0,"syntology":null},{"paper":null,"slug":"rotation-invariant-aerial-image-retrieval","title":"Rotation Invariant Aerial Image Retrieval with Group Convolutional Metric Learning","date":"2020-10-19","arxiv_id":"2010.09202","n_code_links":0,"syntology":null},{"paper":null,"slug":"selfvoxelo-self-supervised-lidar-odometry","title":"SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks","date":"2020-10-19","arxiv_id":"2010.09343","n_code_links":0,"syntology":null},{"paper":"/paper/disguising-personal-identity-information-in","slug":"disguising-personal-identity-information-in","title":"Disguising Personal Identity Information in EEG Signals","date":"2020-10-18","arxiv_id":"2010.08915","n_code_links":1,"syntology":null},{"paper":null,"slug":"distortion-aware-monocular-depth-estimation","title":"Distortion-aware Monocular Depth Estimation for Omnidirectional Images","date":"2020-10-18","arxiv_id":"2010.08942","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-agent-reinforcement-learning-in-noma","title":"Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading","date":"2020-10-18","arxiv_id":"2010.09094","n_code_links":0,"syntology":null},{"paper":null,"slug":"noma-in-uav-aided-cellular-offloading-a","title":"NOMA in UAV-aided cellular offloading: A machine learning approach","date":"2020-10-18","arxiv_id":"2011.14776","n_code_links":0,"syntology":null},{"paper":"/paper/self-attention-generative-adversarial-network","slug":"self-attention-generative-adversarial-network","title":"Self-Attention Generative Adversarial Network for Speech Enhancement","date":"2020-10-18","arxiv_id":"2010.09132","n_code_links":1,"syntology":null},{"paper":null,"slug":"meshmvs-multi-view-stereo-guided-mesh-1","title":"MeshMVS: Multi-View Stereo Guided Mesh Reconstruction","date":"2020-10-17","arxiv_id":"2010.08682","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-iterative-training-of-convolutional","title":"Minimizing Labeling Effort for Tree Skeleton Segmentation using an Automated Iterative Training Methodology","date":"2020-10-16","arxiv_id":"2010.08296","n_code_links":0,"syntology":null},{"paper":"/paper/deep-hoseq-deep-higher-order-sequence-fusion","slug":"deep-hoseq-deep-higher-order-sequence-fusion","title":"Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment Analysis","date":"2020-10-16","arxiv_id":"2010.08218","n_code_links":1,"syntology":null},{"paper":null,"slug":"dpattack-diffused-patch-attacks-against","title":"DPAttack: Diffused Patch Attacks against Universal Object Detection","date":"2020-10-16","arxiv_id":"2010.11679","n_code_links":0,"syntology":null},{"paper":"/paper/for-self-supervised-learning-rationality-1","slug":"for-self-supervised-learning-rationality-1","title":"For self-supervised learning, Rationality implies generalization, provably","date":"2020-10-16","arxiv_id":"2010.08508","n_code_links":2,"syntology":null},{"paper":null,"slug":"latent-vector-recovery-of-audio-gans","title":"Latent Vector Recovery of Audio GANs","date":"2020-10-16","arxiv_id":"2010.08534","n_code_links":0,"syntology":null},{"paper":"/paper/learning-panoptic-segmentation-from-instance","slug":"learning-panoptic-segmentation-from-instance","title":"Learning Panoptic Segmentation from Instance Contours","date":"2020-10-16","arxiv_id":"2010.11681","n_code_links":1,"syntology":null},{"paper":null,"slug":"new-ideas-and-trends-in-deep-multimodal","title":"New Ideas and Trends in Deep Multimodal Content Understanding: A Review","date":"2020-10-16","arxiv_id":"2010.08189","n_code_links":0,"syntology":null},{"paper":null,"slug":"volumenet-a-lightweight-parallel-network-for","title":"VolumeNet: A Lightweight Parallel Network for Super-Resolution of Medical Volumetric Data","date":"2020-10-16","arxiv_id":"2010.08357","n_code_links":0,"syntology":null},{"paper":"/paper/a-comparative-study-on-efficiencies-of","slug":"a-comparative-study-on-efficiencies-of","title":"A Comparative Study on Efficiencies of Variants of Convolutional Neural Networks based on Image Classification Task","date":"2020-10-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"a-deep-drift-diffusion-model-for-image","title":"A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction","date":"2020-10-15","arxiv_id":"2010.07661","n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-based-bmi-inference-from-facial-images-an","title":"AI-based BMI Inference from Facial Images: An Application to Weight Monitoring","date":"2020-10-15","arxiv_id":"2010.07442","n_code_links":0,"syntology":null},{"paper":"/paper/cs2-net-deep-learning-segmentation-of","slug":"cs2-net-deep-learning-segmentation-of","title":"CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging","date":"2020-10-15","arxiv_id":"2010.07486","n_code_links":1,"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":["iMED-Lab/CS-Net"],"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/hs-resnet-hierarchical-split-block-on","slug":"hs-resnet-hierarchical-split-block-on","title":"HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network","date":"2020-10-15","arxiv_id":"2010.07621","n_code_links":2,"syntology":null},{"paper":null,"slug":"litedepthwisenet-an-extreme-lightweight","title":"LiteDepthwiseNet: An Extreme Lightweight Network for Hyperspectral Image Classification","date":"2020-10-15","arxiv_id":"2010.07726","n_code_links":0,"syntology":null},{"paper":null,"slug":"recurrent-distributed-reinforcement-learning","title":"A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly","date":"2020-10-15","arxiv_id":"2010.08052","n_code_links":0,"syntology":null},{"paper":null,"slug":"revisiting-optical-flow-estimation-in-360","title":"Revisiting Optical Flow Estimation in 360 Videos","date":"2020-10-15","arxiv_id":"2010.08045","n_code_links":0,"syntology":null},{"paper":null,"slug":"why-layer-wise-learning-is-hard-to-scale-up","title":"Why Layer-Wise Learning is Hard to Scale-up and a Possible Solution via Accelerated Downsampling","date":"2020-10-15","arxiv_id":"2010.08038","n_code_links":0,"syntology":null},{"paper":"/paper/covid-ct-mask-net-prediction-of-covid-19-from","slug":"covid-ct-mask-net-prediction-of-covid-19-from","title":"COVID-CT-Mask-Net: Prediction of COVID-19 from CT Scans Using Regional Features","date":"2020-10-14","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/do-end-to-end-stereo-algorithms-under-utilize","slug":"do-end-to-end-stereo-algorithms-under-utilize","title":"Do End-to-end Stereo Algorithms Under-utilize Information?","date":"2020-10-14","arxiv_id":"2010.07350","n_code_links":1,"syntology":null},{"paper":"/paper/fast-meningioma-segmentation-in-t1-weighted","slug":"fast-meningioma-segmentation-in-t1-weighted","title":"Fast meningioma segmentation in T1-weighted MRI volumes using a lightweight 3D deep learning architecture","date":"2020-10-14","arxiv_id":"2010.07002","n_code_links":1,"syntology":null},{"paper":null,"slug":"modulation-pattern-detection-using-complex","title":"Modulation Pattern Detection Using Complex Convolutions in Deep Learning","date":"2020-10-14","arxiv_id":"2010.15556","n_code_links":0,"syntology":null},{"paper":"/paper/polar-deconvolution-of-mixed-signals","slug":"polar-deconvolution-of-mixed-signals","title":"Polar Deconvolution of Mixed Signals","date":"2020-10-14","arxiv_id":"2010.10508","n_code_links":1,"syntology":null},{"paper":null,"slug":"self-supervised-ranking-for-representation","title":"Self-Supervised Ranking for Representation Learning","date":"2020-10-14","arxiv_id":"2010.07258","n_code_links":0,"syntology":null},{"paper":null,"slug":"semantic-segmentation-for-partially-occluded","title":"Semantic Segmentation for Partially Occluded Apple Trees Based on Deep Learning","date":"2020-10-14","arxiv_id":"2010.06879","n_code_links":0,"syntology":null},{"paper":"/paper/viewmaker-networks-learning-views-for-1","slug":"viewmaker-networks-learning-views-for-1","title":"Viewmaker Networks: Learning Views for Unsupervised Representation Learning","date":"2020-10-14","arxiv_id":"2010.07432","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["alextamkin/viewmaker"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/attn-hybridnet-improving-discriminability-of","slug":"attn-hybridnet-improving-discriminability-of","title":"Attn-HybridNet: Improving Discriminability of Hybrid Features with Attention Fusion","date":"2020-10-13","arxiv_id":"2010.06096","n_code_links":2,"syntology":null},{"paper":null,"slug":"checkerboard-artifact-free-image-enhancement","title":"Checkerboard-Artifact-Free Image-Enhancement Network Considering Local and Global Features","date":"2020-10-13","arxiv_id":"2010.12347","n_code_links":0,"syntology":null},{"paper":null,"slug":"land-cover-semantic-segmentation-using","title":"Land Cover Semantic Segmentation Using ResUNet","date":"2020-10-13","arxiv_id":"2010.06285","n_code_links":0,"syntology":null},{"paper":"/paper/a-drl-based-multiagent-cooperative-control","slug":"a-drl-based-multiagent-cooperative-control","title":"A DRL-based Multiagent Cooperative Control Framework for CAV Networks: a Graphic Convolution Q Network","date":"2020-10-12","arxiv_id":"2010.05437","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-quantification-of-settlement-damage","title":"Automatic Quantification of Settlement Damage using Deep Learning of Satellite Images","date":"2020-10-12","arxiv_id":"2010.05512","n_code_links":0,"syntology":null},{"paper":null,"slug":"category-specific-semantic-coherency-learning","title":"Category-specific Semantic Coherency Learning for Fine-grained Image Recognition","date":"2020-10-12","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"conditioning-trick-for-training-stable-gans-1","title":"Conditioning Trick for Training Stable GANs","date":"2020-10-12","arxiv_id":"2010.05844","n_code_links":0,"syntology":null},{"paper":"/paper/fully-automatic-wound-segmentation-with-deep","slug":"fully-automatic-wound-segmentation-with-deep","title":"Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks","date":"2020-10-12","arxiv_id":"2010.05855","n_code_links":1,"syntology":null},{"paper":null,"slug":"increasing-the-robustness-of-semantic-1","title":"Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers","date":"2020-10-12","arxiv_id":"2010.05495","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-the-capabilities-of-connected-and","title":"Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion","date":"2020-10-12","arxiv_id":"2010.05436","n_code_links":0,"syntology":null},{"paper":"/paper/locality-preserving-dense-graph-convolutional","slug":"locality-preserving-dense-graph-convolutional","title":"Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations","date":"2020-10-12","arxiv_id":"2010.05404","n_code_links":1,"syntology":null},{"paper":"/paper/omni-directional-image-generation-from-single","slug":"omni-directional-image-generation-from-single","title":"Omni-Directional Image Generation from Single Snapshot Image","date":"2020-10-12","arxiv_id":"2010.05600","n_code_links":2,"syntology":null},{"paper":null,"slug":"reconstruction-of-quantitative-susceptibility","title":"Reconstruction of Quantitative Susceptibility Maps from Phase of Susceptibility Weighted Imaging with Cross-Connected $Ψ$-Net","date":"2020-10-12","arxiv_id":"2010.05395","n_code_links":0,"syntology":null}],"record_sha256":"bc464c8d63bcdd80a9316669d5972bc82cbe9f85e062246a2274353587cf490c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}