{"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/71","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":71,"pages_in_order":196,"rows_per_page":100,"rows":[7001,7100],"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/70","next":"/method/convolution/papers/72","papers":[{"paper":null,"slug":"analog-image-denoising-with-an-adaptive","title":"Analog Image Denoising with an Adaptive Memristive Crossbar Network","date":"2022-09-25","arxiv_id":"2209.12259","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-forward-and-reverse-primer","title":"Deep learning forward and reverse primer design to detect SARS-CoV-2 emerging variants","date":"2022-09-25","arxiv_id":"2209.13591","n_code_links":0,"syntology":null},{"paper":null,"slug":"controllable-face-manipulation-and-uv-map","title":"Controllable Face Manipulation and UV Map Generation by Self-supervised Learning","date":"2022-09-24","arxiv_id":"2209.12050","n_code_links":0,"syntology":null},{"paper":"/paper/cryptogcn-fast-and-scalable-homomorphically","slug":"cryptogcn-fast-and-scalable-homomorphically","title":"CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference","date":"2022-09-24","arxiv_id":"2209.11904","n_code_links":2,"syntology":{"ran":5,"of":7,"n_ran_checked":4,"n_instrument":1,"unverified":2,"pointer_only":7,"phrase":"5 ran (of which 3 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) · 2 unverified","official":{"repos":["ranran0523/cryptogcn"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"development-of-ai-cloud-based-high","title":"Development of AI-cloud based high-sensitivity wireless smart sensor for port structure monitoring","date":"2022-09-24","arxiv_id":"2209.13646","n_code_links":0,"syntology":null},{"paper":null,"slug":"hybrid-multimodal-fusion-for-humor-detection","title":"Hybrid Multimodal Fusion for Humor Detection","date":"2022-09-24","arxiv_id":"2209.11949","n_code_links":0,"syntology":null},{"paper":null,"slug":"image-classification-using-sequence-of-pixels","title":"Image Classification using Sequence of Pixels","date":"2022-09-23","arxiv_id":"2209.11495","n_code_links":0,"syntology":null},{"paper":"/paper/modular-degradation-simulation-and","slug":"modular-degradation-simulation-and","title":"Modular Degradation Simulation and Restoration for Under-Display Camera","date":"2022-09-23","arxiv_id":"2209.11455","n_code_links":1,"syntology":null},{"paper":"/paper/on-efficient-reinforcement-learning-for-full","slug":"on-efficient-reinforcement-learning-for-full","title":"On Efficient Reinforcement Learning for Full-length Game of StarCraft II","date":"2022-09-23","arxiv_id":"2209.11553","n_code_links":2,"syntology":{"ran":8,"of":10,"n_ran_checked":8,"n_instrument":0,"unverified":2,"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) · 2 unverified","official":{"repos":["liuruoze/hiernet-sc2","liuruoze/mini-AlphaStar"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"physics-informed-graph-neural-network-for","title":"Physics-Informed Graph Neural Network for Spatial-temporal Production Forecasting","date":"2022-09-23","arxiv_id":"2209.11885","n_code_links":0,"syntology":null},{"paper":"/paper/query-based-hard-image-retrieval-for-object","slug":"query-based-hard-image-retrieval-for-object","title":"Query-based Hard-Image Retrieval for Object Detection at Test Time","date":"2022-09-23","arxiv_id":"2209.11559","n_code_links":1,"syntology":null},{"paper":"/paper/rethinking-performance-gains-in-image","slug":"rethinking-performance-gains-in-image","title":"Rethinking Performance Gains in Image Dehazing Networks","date":"2022-09-23","arxiv_id":"2209.11448","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-kriston-ai-system-for-the-voxceleb","title":"The Kriston AI System for the VoxCeleb Speaker Recognition Challenge 2022","date":"2022-09-23","arxiv_id":"2209.11433","n_code_links":0,"syntology":null},{"paper":null,"slug":"tiered-pruning-for-efficient-differentialble","title":"Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search","date":"2022-09-23","arxiv_id":"2209.11785","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-complete-view-and-high-level-pose","title":"Towards Complete-View and High-Level Pose-based Gait Recognition","date":"2022-09-23","arxiv_id":"2209.11577","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-microbubble-localization","title":"Transformer-Based Microbubble Localization","date":"2022-09-23","arxiv_id":"2209.11859","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-ct-based-airway-segmentation-using-u-2-net","title":"A CT-Based Airway Segmentation Using U$^2$-net Trained by the Dice Loss Function","date":"2022-09-22","arxiv_id":"2209.10796","n_code_links":0,"syntology":null},{"paper":"/paper/calving-fronts-and-where-to-find-them-a","slug":"calving-fronts-and-where-to-find-them-a","title":"Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery","date":"2022-09-22","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/dlunet-semi-supervised-learning-based-dual","slug":"dlunet-semi-supervised-learning-based-dual","title":"DLUNet: Semi-supervised Learning based Dual-Light UNet for Multi-organ Segmentation","date":"2022-09-22","arxiv_id":"2209.10984","n_code_links":1,"syntology":null},{"paper":null,"slug":"drkf-distilled-rotated-kernel-fusion-for","title":"DRKF: Distilled Rotated Kernel Fusion for Efficient Rotation Invariant Descriptors in Local Feature Matching","date":"2022-09-22","arxiv_id":"2209.10907","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-cnn-with-uncorrelated-bag-of","title":"Efficient CNN with uncorrelated Bag of Features pooling","date":"2022-09-22","arxiv_id":"2209.10865","n_code_links":0,"syntology":null},{"paper":null,"slug":"google-coral-based-edge-computing-person","title":"Google Coral-based edge computing person reidentification using human parsing combined with analytical method","date":"2022-09-22","arxiv_id":"2209.11024","n_code_links":0,"syntology":null},{"paper":null,"slug":"hierarchical-graph-convolutional-network","title":"Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity","date":"2022-09-22","arxiv_id":"2209.11232","n_code_links":0,"syntology":null},{"paper":"/paper/learning-invariant-representations-for-2","slug":"learning-invariant-representations-for-2","title":"Learning Invariant Representations for Equivariant Neural Networks Using Orthogonal Moments","date":"2022-09-22","arxiv_id":"2209.10944","n_code_links":1,"syntology":null},{"paper":null,"slug":"minimizing-human-assistance-augmenting-a","title":"Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning","date":"2022-09-22","arxiv_id":"2209.11275","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimization-of-fpga-based-cnn-accelerators","title":"Optimization of FPGA-based CNN Accelerators Using Metaheuristics","date":"2022-09-22","arxiv_id":"2209.11272","n_code_links":0,"syntology":null},{"paper":"/paper/physics-informed-convolutional-transformer","slug":"physics-informed-convolutional-transformer","title":"Physics-Informed Convolutional Transformer for Predicting Volatility Surface","date":"2022-09-22","arxiv_id":"2209.10771","n_code_links":1,"syntology":null},{"paper":"/paper/vtoonify-controllable-high-resolution","slug":"vtoonify-controllable-high-resolution","title":"VToonify: Controllable High-Resolution Portrait Video Style Transfer","date":"2022-09-22","arxiv_id":"2209.11224","n_code_links":1,"syntology":null},{"paper":"/paper/convolutional-bayesian-kernel-inference-for","slug":"convolutional-bayesian-kernel-inference-for","title":"Convolutional Bayesian Kernel Inference for 3D Semantic Mapping","date":"2022-09-21","arxiv_id":"2209.10663","n_code_links":2,"syntology":null},{"paper":"/paper/hifuse-hierarchical-multi-scale-feature","slug":"hifuse-hierarchical-multi-scale-feature","title":"HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image Classification","date":"2022-09-21","arxiv_id":"2209.10218","n_code_links":1,"syntology":null},{"paper":null,"slug":"lamarckian-platform-pushing-the-boundaries-of","title":"Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games","date":"2022-09-21","arxiv_id":"2209.10055","n_code_links":0,"syntology":null},{"paper":null,"slug":"mandarin-singing-voice-synthesis-with","title":"Mandarin Singing Voice Synthesis with Denoising Diffusion Probabilistic Wasserstein GAN","date":"2022-09-21","arxiv_id":"2209.10446","n_code_links":0,"syntology":null},{"paper":null,"slug":"model-free-reinforcement-learning-for-asset","title":"Model-Free Reinforcement Learning for Asset Allocation","date":"2022-09-21","arxiv_id":"2209.10458","n_code_links":0,"syntology":null},{"paper":null,"slug":"modeling-cognitive-load-as-a-self-supervised","title":"Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning","date":"2022-09-21","arxiv_id":"2209.10992","n_code_links":0,"syntology":null},{"paper":"/paper/multi-field-de-interlacing-using-deformable","slug":"multi-field-de-interlacing-using-deformable","title":"Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention","date":"2022-09-21","arxiv_id":"2209.10192","n_code_links":0,"syntology":null},{"paper":null,"slug":"near-field-sar-image-restoration-based-on-two","title":"Near-Field SAR Image Restoration Based On Two Dimensional Spatial-Variant Deconvolution","date":"2022-09-21","arxiv_id":"2209.10442","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-discrete-soft-actor-critic","slug":"revisiting-discrete-soft-actor-critic","title":"Revisiting Discrete Soft Actor-Critic","date":"2022-09-21","arxiv_id":"2209.10081","n_code_links":1,"syntology":null},{"paper":"/paper/sda-x-net-selective-depth-attention-networks","slug":"sda-x-net-selective-depth-attention-networks","title":"SDA-$x$Net: Selective Depth Attention Networks for Adaptive Multi-scale Feature Representation","date":"2022-09-21","arxiv_id":"2209.10327","n_code_links":1,"syntology":null},{"paper":"/paper/cov-ti-net-transferred-initialization-with","slug":"cov-ti-net-transferred-initialization-with","title":"CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis","date":"2022-09-20","arxiv_id":"2209.09556","n_code_links":1,"syntology":null},{"paper":"/paper/dynamic-graph-message-passing-networks-for","slug":"dynamic-graph-message-passing-networks-for","title":"Dynamic Graph Message Passing Networks for Visual Recognition","date":"2022-09-20","arxiv_id":"2209.09760","n_code_links":2,"syntology":null},{"paper":"/paper/a-dual-cycled-cross-view-transformer-network","slug":"a-dual-cycled-cross-view-transformer-network","title":"A Dual-Cycled Cross-View Transformer Network for Unified Road Layout Estimation and 3D Object Detection in the Bird's-Eye-View","date":"2022-09-19","arxiv_id":"2209.08844","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-trio-method-for-retinal-vessel-segmentation","title":"A Trio-Method for Retinal Vessel Segmentation using Image Processing","date":"2022-09-19","arxiv_id":"2209.11230","n_code_links":0,"syntology":null},{"paper":null,"slug":"effective-adaptation-in-multi-task-co","title":"Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving","date":"2022-09-19","arxiv_id":"2209.08953","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-approach-of-using-cnn-based","title":"Efficient approach of using CNN based pretrained model in Bangla handwritten digit recognition","date":"2022-09-19","arxiv_id":"2209.13005","n_code_links":0,"syntology":null},{"paper":null,"slug":"estimating-brain-age-with-global-and-local","title":"Estimating Brain Age with Global and Local Dependencies","date":"2022-09-19","arxiv_id":"2209.08933","n_code_links":0,"syntology":null},{"paper":null,"slug":"lgc-net-a-lightweight-gyroscope-calibration","title":"LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude Estimation","date":"2022-09-19","arxiv_id":"2209.08816","n_code_links":0,"syntology":null},{"paper":"/paper/masked-face-inpainting-through-residual","slug":"masked-face-inpainting-through-residual","title":"Masked Face Inpainting Through Residual Attention UNet","date":"2022-09-19","arxiv_id":"2209.08850","n_code_links":1,"syntology":null},{"paper":null,"slug":"msa-gcn-multiscale-adaptive-graph-convolution","title":"MSA-GCN:Multiscale Adaptive Graph Convolution Network for Gait Emotion Recognition","date":"2022-09-19","arxiv_id":"2209.08988","n_code_links":0,"syntology":null},{"paper":"/paper/energy-efficient-automatic-streetlight","slug":"energy-efficient-automatic-streetlight","title":"CNN based Intelligent Streetlight Management Using Smart CCTV Camera and Semantic Segmentation","date":"2022-09-18","arxiv_id":"2209.08633","n_code_links":1,"syntology":null},{"paper":"/paper/rvsl-robust-vehicle-similarity-learning-in","slug":"rvsl-robust-vehicle-similarity-learning-in","title":"RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning","date":"2022-09-18","arxiv_id":"2209.08630","n_code_links":1,"syntology":null},{"paper":"/paper/segnext-rethinking-convolutional-attention","slug":"segnext-rethinking-convolutional-attention","title":"SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation","date":"2022-09-18","arxiv_id":"2209.08575","n_code_links":5,"syntology":null},{"paper":null,"slug":"simplifying-model-based-rl-learning","title":"Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective","date":"2022-09-18","arxiv_id":"2209.08466","n_code_links":0,"syntology":null},{"paper":null,"slug":"through-a-fair-looking-glass-mitigating-bias","title":"Through a fair looking-glass: mitigating bias in image datasets","date":"2022-09-18","arxiv_id":"2209.08648","n_code_links":0,"syntology":null},{"paper":"/paper/automated-segmentation-and-recurrence-risk","slug":"automated-segmentation-and-recurrence-risk","title":"Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks","date":"2022-09-17","arxiv_id":"2209.08423","n_code_links":1,"syntology":null},{"paper":null,"slug":"continuously-controllable-facial-expression","title":"Continuously Controllable Facial Expression Editing in Talking Face Videos","date":"2022-09-17","arxiv_id":"2209.08289","n_code_links":0,"syntology":null},{"paper":"/paper/deep-plug-and-play-prior-for-hyperspectral","slug":"deep-plug-and-play-prior-for-hyperspectral","title":"Deep Plug-and-Play Prior for Hyperspectral Image Restoration","date":"2022-09-17","arxiv_id":"2209.08240","n_code_links":1,"syntology":null},{"paper":null,"slug":"inducing-early-neural-collapse-in-deep-neural","title":"Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks","date":"2022-09-17","arxiv_id":"2209.08378","n_code_links":0,"syntology":null},{"paper":null,"slug":"anomaly-detection-in-automatic-generation","title":"Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning","date":"2022-09-16","arxiv_id":"2209.08099","n_code_links":0,"syntology":null},{"paper":null,"slug":"causal-fourier-analysis-on-directed-acyclic","title":"Causal Fourier Analysis on Directed Acyclic Graphs and Posets","date":"2022-09-16","arxiv_id":"2209.07970","n_code_links":0,"syntology":null},{"paper":"/paper/expansion-and-shrinkage-of-localization-for","slug":"expansion-and-shrinkage-of-localization-for","title":"Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation","date":"2022-09-16","arxiv_id":"2209.07761","n_code_links":1,"syntology":null},{"paper":"/paper/exploring-stylegan-latent-space-for-face","slug":"exploring-stylegan-latent-space-for-face","title":"Exploring StyleGAN Latent Space for Face Alignment with Limited Training Data","date":"2022-09-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"extrapolation-and-spectral-bias-of-neural","title":"Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study","date":"2022-09-16","arxiv_id":"2209.07736","n_code_links":0,"syntology":null},{"paper":"/paper/imitrob-imitation-learning-dataset-for","slug":"imitrob-imitation-learning-dataset-for","title":"Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators","date":"2022-09-16","arxiv_id":"2209.07976","n_code_links":1,"syntology":null},{"paper":null,"slug":"lo-det-lightweight-oriented-object-detection","title":"LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images","date":"2022-09-16","arxiv_id":"2209.07709","n_code_links":0,"syntology":null},{"paper":"/paper/m-2-dqn-a-robust-method-for-accelerating-deep","slug":"m-2-dqn-a-robust-method-for-accelerating-deep","title":"M$^2$DQN: A Robust Method for Accelerating Deep Q-learning Network","date":"2022-09-16","arxiv_id":"2209.07809","n_code_links":1,"syntology":null},{"paper":"/paper/omni-dimensional-dynamic-convolution-1","slug":"omni-dimensional-dynamic-convolution-1","title":"Omni-Dimensional Dynamic Convolution","date":"2022-09-16","arxiv_id":"2209.07947","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 2 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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["osvai/odconv"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/reducing-variance-in-temporal-difference","slug":"reducing-variance-in-temporal-difference","title":"Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks","date":"2022-09-16","arxiv_id":"2209.07670","n_code_links":1,"syntology":null},{"paper":null,"slug":"stylegan-encoder-based-attack-for-block","title":"StyleGAN Encoder-Based Attack for Block Scrambled Face Images","date":"2022-09-16","arxiv_id":"2209.07953","n_code_links":0,"syntology":null},{"paper":null,"slug":"ffpa-net-efficient-feature-fusion-with","title":"FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection","date":"2022-09-15","arxiv_id":"2209.07419","n_code_links":0,"syntology":null},{"paper":"/paper/hardnet-dfus-an-enhanced-harmonically","slug":"hardnet-dfus-an-enhanced-harmonically","title":"HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation","date":"2022-09-15","arxiv_id":"2209.07313","n_code_links":2,"syntology":null},{"paper":"/paper/human-level-atari-200x-faster","slug":"human-level-atari-200x-faster","title":"Human-level Atari 200x faster","date":"2022-09-15","arxiv_id":"2209.07550","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"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) · 2 unverified","official":null}},{"paper":"/paper/improving-mitosis-detection-via-unet-based","slug":"improving-mitosis-detection-via-unet-based","title":"Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer","date":"2022-09-15","arxiv_id":"2209.09193","n_code_links":1,"syntology":null},{"paper":"/paper/nu-net-an-unpretentious-nested-u-net-for","slug":"nu-net-an-unpretentious-nested-u-net-for","title":"Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation","date":"2022-09-15","arxiv_id":"2209.07193","n_code_links":2,"syntology":null},{"paper":null,"slug":"prob-slam-real-time-visual-slam-based-on","title":"PROB-SLAM: Real-time Visual SLAM Based on Probabilistic Graph Optimization","date":"2022-09-15","arxiv_id":"2209.07061","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-development-of-spatial-attention-u-net","title":"The Development of Spatial Attention U-Net for The Recovery of Ionospheric Measurements and The Extraction of Ionospheric Parameters","date":"2022-09-15","arxiv_id":"2209.07581","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-temporal-anomaly-detection-system-for","title":"A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels","date":"2022-09-14","arxiv_id":"2209.06828","n_code_links":0,"syntology":null},{"paper":"/paper/fcdsn-dc-an-accurate-and-lightweight","slug":"fcdsn-dc-an-accurate-and-lightweight","title":"FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth Completion","date":"2022-09-14","arxiv_id":"2209.06525","n_code_links":1,"syntology":null},{"paper":"/paper/generative-visual-prompt-unifying","slug":"generative-visual-prompt-unifying","title":"Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models","date":"2022-09-14","arxiv_id":"2209.06970","n_code_links":1,"syntology":null},{"paper":"/paper/label-refinement-network-from-synthetic-error","slug":"label-refinement-network-from-synthetic-error","title":"Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation","date":"2022-09-14","arxiv_id":"2209.06353","n_code_links":1,"syntology":null},{"paper":null,"slug":"landmark-tracking-in-liver-us-images-using","title":"Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory","date":"2022-09-14","arxiv_id":"2209.06952","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-interplay-of-adversarial-robustness","title":"On the interplay of adversarial robustness and architecture components: patches, convolution and attention","date":"2022-09-14","arxiv_id":"2209.06953","n_code_links":0,"syntology":null},{"paper":null,"slug":"tasked-transformer-based-adversarial-learning","title":"TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation","date":"2022-09-14","arxiv_id":"2209.09092","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-lightweight-transformer-based-model-for","title":"A lightweight Transformer-based model for fish landmark detection","date":"2022-09-13","arxiv_id":"2209.05777","n_code_links":0,"syntology":null},{"paper":null,"slug":"analyzing-the-impact-of-varied-window-hyper","title":"Analyzing the Impact of Varied Window Hyper-parameters on Deep CNN for sEMG based Motion Intent Classification","date":"2022-09-13","arxiv_id":"2209.05804","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-segmentation-and","title":"Comparative analysis of segmentation and generative models for fingerprint retrieval task","date":"2022-09-13","arxiv_id":"2209.06172","n_code_links":0,"syntology":null},{"paper":null,"slug":"completr-reducing-the-cost-of-annotations-for","title":"ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers","date":"2022-09-13","arxiv_id":"2209.05654","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-training-on-multi-instance-gpus","title":"An Analysis of Collocation on GPUs for Deep Learning Training","date":"2022-09-13","arxiv_id":"2209.06018","n_code_links":0,"syntology":null},{"paper":null,"slug":"document-image-binarization-in-jpeg","title":"Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks","date":"2022-09-13","arxiv_id":"2209.05921","n_code_links":0,"syntology":null},{"paper":null,"slug":"histoperm-a-permutation-based-view-generation","title":"HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning","date":"2022-09-13","arxiv_id":"2209.06185","n_code_links":0,"syntology":null},{"paper":"/paper/moving-from-2d-to-3d-volumetric-medical-image","slug":"moving-from-2d-to-3d-volumetric-medical-image","title":"Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging","date":"2022-09-13","arxiv_id":"2209.05771","n_code_links":1,"syntology":null},{"paper":"/paper/predicting-brain-multigraph-population-from-a","slug":"predicting-brain-multigraph-population-from-a","title":"Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification","date":"2022-09-13","arxiv_id":"2209.06005","n_code_links":1,"syntology":null},{"paper":"/paper/cu-net-efficient-point-cloud-color-upsampling","slug":"cu-net-efficient-point-cloud-color-upsampling","title":"CU-Net: Real-Time High-Fidelity Color Upsampling for Point Clouds","date":"2022-09-12","arxiv_id":"2209.06112","n_code_links":1,"syntology":null},{"paper":"/paper/detecting-network-based-internet-censorship","slug":"detecting-network-based-internet-censorship","title":"Detecting Network-based Internet Censorship via Latent Feature Representation Learning","date":"2022-09-12","arxiv_id":"2209.05152","n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-polynomial-convolution-models-for-node","title":"Graph Polynomial Convolution Models for Node Classification of Non-Homophilous Graphs","date":"2022-09-12","arxiv_id":"2209.05020","n_code_links":0,"syntology":null},{"paper":"/paper/partial-observability-during-drl-for-robot","slug":"partial-observability-during-drl-for-robot","title":"Experimental Study on The Effect of Multi-step Deep Reinforcement Learning in POMDPs","date":"2022-09-12","arxiv_id":"2209.04999","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-based-doa-estimation-for-hybrid","title":"Deep Learning Based DOA Estimation for Hybrid Massive MIMO Receive Array with Overlapped Subarrays","date":"2022-09-11","arxiv_id":"2209.04806","n_code_links":0,"syntology":null},{"paper":"/paper/git-re-basin-merging-models-modulo","slug":"git-re-basin-merging-models-modulo","title":"Git Re-Basin: Merging Models modulo Permutation Symmetries","date":"2022-09-11","arxiv_id":"2209.04836","n_code_links":3,"syntology":{"ran":6,"of":6,"n_ran_checked":1,"n_instrument":5,"unverified":0,"pointer_only":1,"phrase":"6 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; 5 where Syntology's instrument failed) · 0 unverified","official":{"repos":["samuela/git-re-basin"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"local-aware-global-attention-network-for","title":"Local-Aware Global Attention Network for Person Re-Identification Based on Body and Hand Images","date":"2022-09-11","arxiv_id":"2209.04821","n_code_links":0,"syntology":null},{"paper":"/paper/patching-weak-convolutional-neural-network","slug":"patching-weak-convolutional-neural-network","title":"Patching Weak Convolutional Neural Network Models through Modularization and Composition","date":"2022-09-11","arxiv_id":"2209.06116","n_code_links":1,"syntology":null},{"paper":null,"slug":"pathfinding-in-random-partially-observable","title":"Pathfinding in Random Partially Observable Environments with Vision-Informed Deep Reinforcement Learning","date":"2022-09-11","arxiv_id":"2209.04801","n_code_links":0,"syntology":null}],"record_sha256":"ee53878d4237e028d447110564e81f50cc9fc3f33f5f575b381254df20d76135","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}