{"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/max-pooling/papers/23","list_of":"/method/max-pooling","method":"Max Pooling","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":23,"pages_in_order":72,"rows_per_page":100,"rows":[2201,2300],"of":7126,"counts":{"archive_papers_tagged":7126,"with_a_code_link":2898,"where_syntology_ran_a_sample":640,"not_listed_spam_title":0,"listed":7126,"listed_where_code_ran":640,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":531,"every_run_a_failure_of_syntologys_instrument":109,"listed_with_a_run_with_no_instrument_failure":531,"listed_every_run_a_failure_of_syntologys_instrument":109,"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/max-pooling","prev":"/method/max-pooling/papers/22","next":"/method/max-pooling/papers/24","papers":[{"paper":"/paper/point-cloud-diffusion-models-for-automatic","slug":"point-cloud-diffusion-models-for-automatic","title":"Point Cloud Diffusion Models for Automatic Implant Generation","date":"2023-03-14","arxiv_id":"2303.08061","n_code_links":1,"syntology":null},{"paper":null,"slug":"mirror-u-net-marrying-multimodal-fission-with","title":"Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging","date":"2023-03-13","arxiv_id":"2303.07126","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-class-skin-cancer-classification","title":"Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network","date":"2023-03-13","arxiv_id":"2303.07520","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-convolutional-neural-networks-for-1","title":"Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging","date":"2023-03-13","arxiv_id":"2303.07189","n_code_links":0,"syntology":null},{"paper":"/paper/otov2-automatic-generic-user-friendly","slug":"otov2-automatic-generic-user-friendly","title":"OTOV2: Automatic, Generic, User-Friendly","date":"2023-03-13","arxiv_id":"2303.06862","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["tianyic/only_train_once"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":null,"slug":"the-challenge-of-representation-learning","title":"The challenge of representation learning: Improved accuracy in deep vision models does not come with better predictions of perceptual similarity","date":"2023-03-13","arxiv_id":"2303.07084","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-hdr-image-and-video-tone-mapping","slug":"unsupervised-hdr-image-and-video-tone-mapping","title":"Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning","date":"2023-03-13","arxiv_id":"2303.07327","n_code_links":1,"syntology":null},{"paper":null,"slug":"sequential-spatial-network-for-collision","title":"Sequential Spatial Network for Collision Avoidance in Autonomous Driving","date":"2023-03-12","arxiv_id":"2303.07352","n_code_links":0,"syntology":null},{"paper":"/paper/token-sparsification-for-faster-medical-image","slug":"token-sparsification-for-faster-medical-image","title":"Token Sparsification for Faster Medical Image Segmentation","date":"2023-03-11","arxiv_id":"2303.06522","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-the-success-rates-by-performing","title":"Enhancing the accuracies by performing pooling decisions adjacent to the output layer","date":"2023-03-10","arxiv_id":"2303.05800","n_code_links":0,"syntology":null},{"paper":"/paper/generalized-diffusion-mri-denoising-and-super","slug":"generalized-diffusion-mri-denoising-and-super","title":"Generative AI for Rapid Diffusion MRI with Improved Image Quality, Reliability and Generalizability","date":"2023-03-10","arxiv_id":"2303.05686","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-domain-invariance-in-self","title":"Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization","date":"2023-03-10","arxiv_id":"2303.06088","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-based-detection-of","title":"Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity","date":"2023-03-10","arxiv_id":"2303.11429","n_code_links":0,"syntology":null},{"paper":"/paper/marginalia-and-machine-learning-handwritten","slug":"marginalia-and-machine-learning-handwritten","title":"Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and Recognition","date":"2023-03-10","arxiv_id":"2303.05929","n_code_links":2,"syntology":null},{"paper":null,"slug":"an-evaluation-of-non-contrastive-self","title":"An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis","date":"2023-03-09","arxiv_id":"2303.05556","n_code_links":0,"syntology":null},{"paper":null,"slug":"mdaesf-cine-mri-reconstruction-based-on","title":"Reconstruction of Cardiac Cine MRI Using Motion-Guided Deformable Alignment and Multi-Resolution Fusion","date":"2023-03-09","arxiv_id":"2303.04968","n_code_links":0,"syntology":null},{"paper":null,"slug":"fcn-global-receptive-convolution-makes-fcn","title":"FCN+: Global Receptive Convolution Makes FCN Great Again","date":"2023-03-08","arxiv_id":"2303.04589","n_code_links":0,"syntology":null},{"paper":null,"slug":"toward-polar-sea-ice-classification-using","title":"Toward Polar Sea-Ice Classification using Color-based Segmentation and Auto-labeling of Sentinel-2 Imagery to Train an Efficient Deep Learning Model","date":"2023-03-08","arxiv_id":"2303.12719","n_code_links":0,"syntology":null},{"paper":null,"slug":"ut-net-combining-u-net-and-transformer-for","title":"UT-Net: Combining U-Net and Transformer for Joint Optic Disc and Cup Segmentation and Glaucoma Detection","date":"2023-03-08","arxiv_id":"2303.04939","n_code_links":0,"syntology":null},{"paper":"/paper/a-comparative-study-of-deep-learning-and","slug":"a-comparative-study-of-deep-learning-and","title":"A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection in OFDM Systems","date":"2023-03-07","arxiv_id":"2303.03678","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-computer-vision-enabled-damage-detection","title":"A Computer Vision Enabled damage detection model with improved YOLOv5 based on Transformer Prediction Head","date":"2023-03-07","arxiv_id":"2303.04275","n_code_links":0,"syntology":null},{"paper":null,"slug":"filter-pruning-based-on-information-capacity","title":"Filter Pruning based on Information Capacity and Independence","date":"2023-03-07","arxiv_id":"2303.03645","n_code_links":0,"syntology":null},{"paper":null,"slug":"psdnet-determination-of-particle-size","title":"PSDNet: Determination of Particle Size Distributions Using Synthetic Soil Images and Convolutional Neural Networks","date":"2023-03-07","arxiv_id":"2303.04269","n_code_links":0,"syntology":null},{"paper":null,"slug":"weakly-supervised-realtime-dynamic-background","title":"Weakly Supervised Realtime Dynamic Background Subtraction","date":"2023-03-06","arxiv_id":"2303.02857","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-study-of-transformer-and-lstm","title":"Comparative study of Transformer and LSTM Network with attention mechanism on Image Captioning","date":"2023-03-05","arxiv_id":"2303.02648","n_code_links":0,"syntology":null},{"paper":null,"slug":"discrepancies-among-pre-trained-deep-neural","title":"Discrepancies among Pre-trained Deep Neural Networks: A New Threat to Model Zoo Reliability","date":"2023-03-05","arxiv_id":"2303.02551","n_code_links":0,"syntology":null},{"paper":null,"slug":"hybrid-y-net-architecture-for-singing-voice","title":"Hybrid Y-Net Architecture for Singing Voice Separation","date":"2023-03-05","arxiv_id":"2303.02599","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-based-convolutional-neural-network-3","title":"Attention-based convolutional neural network for perfusion T2-weighted MR images preprocessing","date":"2023-03-04","arxiv_id":"2303.02518","n_code_links":0,"syntology":null},{"paper":"/paper/extended-agriculture-vision-an-extension-of-a","slug":"extended-agriculture-vision-an-extension-of-a","title":"Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis","date":"2023-03-04","arxiv_id":"2303.02460","n_code_links":1,"syntology":null},{"paper":"/paper/benchmarking-white-blood-cell-classification","slug":"benchmarking-white-blood-cell-classification","title":"Benchmarking White Blood Cell Classification Under Domain Shift","date":"2023-03-03","arxiv_id":"2303.01777","n_code_links":1,"syntology":null},{"paper":"/paper/contrastive-hierarchical-clustering","slug":"contrastive-hierarchical-clustering","title":"Contrastive Hierarchical Clustering","date":"2023-03-03","arxiv_id":"2303.03389","n_code_links":1,"syntology":null},{"paper":"/paper/revisiting-adversarial-training-for-imagenet-1","slug":"revisiting-adversarial-training-for-imagenet-1","title":"Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models","date":"2023-03-03","arxiv_id":"2303.01870","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"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":["nmndeep/revisiting-at"],"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/rotation-invariant-quantization-for-model","slug":"rotation-invariant-quantization-for-model","title":"Rotation Invariant Quantization for Model Compression","date":"2023-03-03","arxiv_id":"2303.03106","n_code_links":1,"syntology":null},{"paper":"/paper/towards-democratizing-joint-embedding-self","slug":"towards-democratizing-joint-embedding-self","title":"Towards Democratizing Joint-Embedding Self-Supervised Learning","date":"2023-03-03","arxiv_id":"2303.01986","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/ffcv-ssl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/unified-perception-efficient-video-panoptic","slug":"unified-perception-efficient-video-panoptic","title":"Unified Perception: Efficient Depth-Aware Video Panoptic Segmentation with Minimal Annotation Costs","date":"2023-03-03","arxiv_id":"2303.01991","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-few-shot-attention-recurrent-residual-u-net","title":"A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation","date":"2023-03-02","arxiv_id":"2303.01582","n_code_links":0,"syntology":null},{"paper":"/paper/contextual-net-a-multimodal-vision-language","slug":"contextual-net-a-multimodal-vision-language","title":"ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax","date":"2023-03-02","arxiv_id":"2303.01615","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-based-segmentation-of-optical","title":"Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein","date":"2023-03-02","arxiv_id":"2303.01054","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-of-drain-a-deep-learning-approach","title":"Evaluation of drain, a deep-learning approach to rain retrieval from gpm passive microwave radiometer","date":"2023-03-02","arxiv_id":"2303.01220","n_code_links":0,"syntology":null},{"paper":null,"slug":"paraformer-parallel-attention-transformer-for","title":"ParaFormer: Parallel Attention Transformer for Efficient Feature Matching","date":"2023-03-02","arxiv_id":"2303.00941","n_code_links":0,"syntology":null},{"paper":"/paper/dan-nucnet-a-dual-attention-based-framework","slug":"dan-nucnet-a-dual-attention-based-framework","title":"DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions","date":"2023-03-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"predicting-the-wall-shear-stress-and-wall","title":"Predicting the wall-shear stress and wall pressure through convolutional neural networks","date":"2023-03-01","arxiv_id":"2303.00706","n_code_links":0,"syntology":null},{"paper":null,"slug":"speeding-up-efficientnet-selecting-update","title":"Speeding Up EfficientNet: Selecting Update Blocks of Convolutional Neural Networks using Genetic Algorithm in Transfer Learning","date":"2023-03-01","arxiv_id":"2303.00261","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-for-identifying-iran-s-cultural","slug":"deep-learning-for-identifying-iran-s-cultural","title":"Deep Learning for Identifying Iran's Cultural Heritage Buildings in Need of Conservation Using Image Classification and Grad-CAM","date":"2023-02-28","arxiv_id":"2302.14354","n_code_links":1,"syntology":null},{"paper":null,"slug":"gran-ghost-residual-attention-network-for","title":"GRAN: Ghost Residual Attention Network for Single Image Super Resolution","date":"2023-02-28","arxiv_id":"2302.14557","n_code_links":0,"syntology":null},{"paper":null,"slug":"opto-unet-optimized-unet-for-segmentation-of","title":"Opto-UNet: Optimized UNet for Segmentation of Varicose Veins in Optical Coherence Tomography","date":"2023-02-28","arxiv_id":"2302.14808","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-the-performance-of-a-computing","title":"Predicting the Performance of a Computing System with Deep Networks","date":"2023-02-27","arxiv_id":"2302.13638","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatial-frequency-attention-for-image","title":"Spatial-Frequency Attention for Image Denoising","date":"2023-02-27","arxiv_id":"2302.13598","n_code_links":0,"syntology":null},{"paper":null,"slug":"supervised-virtual-to-real-domain-adaptation","title":"Supervised Virtual-to-Real Domain Adaptation for Object Detection Task using YOLO","date":"2023-02-27","arxiv_id":"2302.13891","n_code_links":0,"syntology":null},{"paper":"/paper/learning-pairwise-interaction-for","slug":"learning-pairwise-interaction-for","title":"Learning Pairwise Interaction for Generalizable DeepFake Detection","date":"2023-02-26","arxiv_id":"2302.13288","n_code_links":1,"syntology":null},{"paper":null,"slug":"transferd2-automated-defect-detection","title":"TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques","date":"2023-02-26","arxiv_id":"2302.13317","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-light-weight-deep-learning-model-for-remote","title":"A Light-weight Deep Learning Model for Remote Sensing Image Classification","date":"2023-02-25","arxiv_id":"2302.13028","n_code_links":0,"syntology":null},{"paper":"/paper/amortised-invariance-learning-for-contrastive","slug":"amortised-invariance-learning-for-contrastive","title":"Amortised Invariance Learning for Contrastive Self-Supervision","date":"2023-02-24","arxiv_id":"2302.12712","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":2,"phrase":"0 ran · 2 unverified","official":{"repos":["ruchikachavhan/amortized-invariance-learning-ssl"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":null,"slug":"automatic-classification-of-symmetry-of","title":"Automatic Classification of Symmetry of Hemithoraces in Canine and Feline Radiographs","date":"2023-02-24","arxiv_id":"2302.12923","n_code_links":0,"syntology":null},{"paper":null,"slug":"cosmic-microwave-background-recovery-a-graph","title":"Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach","date":"2023-02-24","arxiv_id":"2302.12378","n_code_links":0,"syntology":null},{"paper":null,"slug":"crossing-points-detection-in-plain-weave-for","title":"Crossing Points Detection in Plain Weave for Old Paintings with Deep Learning","date":"2023-02-23","arxiv_id":"2302.11924","n_code_links":0,"syntology":null},{"paper":"/paper/dermatological-diagnosis-explainability","slug":"dermatological-diagnosis-explainability","title":"Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks","date":"2023-02-23","arxiv_id":"2302.12084","n_code_links":1,"syntology":null},{"paper":null,"slug":"plu-net-extraction-of-multi-scale-feature","title":"PLU-Net: Extraction of multi-scale feature fusion","date":"2023-02-23","arxiv_id":"2302.11806","n_code_links":0,"syntology":null},{"paper":"/paper/magnification-invariant-medical-image","slug":"magnification-invariant-medical-image","title":"Magnification Invariant Medical Image Analysis: A Comparison of Convolutional Networks, Vision Transformers, and Token Mixers","date":"2023-02-22","arxiv_id":"2302.11488","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-segmentation-of-multi-vendor-1","title":"Semi-Supervised Segmentation of Multi-vendor and Multi-center Cardiac MRI using Histogram Matching","date":"2023-02-22","arxiv_id":"2302.11200","n_code_links":0,"syntology":null},{"paper":null,"slug":"slim-u-net-efficient-anatomical-feature","title":"Slim U-Net: Efficient Anatomical Feature Preserving U-net Architecture for Ultrasound Image Segmentation","date":"2023-02-22","arxiv_id":"2302.11524","n_code_links":0,"syntology":null},{"paper":null,"slug":"use-cases-for-time-frequency-image","title":"Use Cases for Time-Frequency Image Representations and Deep Learning Techniques for Improved Signal Classification","date":"2023-02-22","arxiv_id":"2302.11093","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-learning-based-and-fully-automated","title":"A Deep Learning-Based Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis From 3D Echocardiography","date":"2023-02-21","arxiv_id":"2302.10634","n_code_links":0,"syntology":null},{"paper":null,"slug":"effects-of-architectures-on-continual","title":"Effects of Architectures on Continual Semantic Segmentation","date":"2023-02-21","arxiv_id":"2302.10718","n_code_links":0,"syntology":null},{"paper":"/paper/su-net-pose-estimation-network-for-non","slug":"su-net-pose-estimation-network-for-non","title":"SU-Net: Pose estimation network for non-cooperative spacecraft on-orbit","date":"2023-02-21","arxiv_id":"2302.10602","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-incremental-gray-box-physical-adversarial","title":"An Incremental Gray-box Physical Adversarial Attack on Neural Network Training","date":"2023-02-20","arxiv_id":"2303.01245","n_code_links":0,"syntology":null},{"paper":null,"slug":"augmenting-endometriosis-analysis-from","title":"Augmenting endometriosis analysis from ultrasound data with deep learning","date":"2023-02-19","arxiv_id":"2302.09621","n_code_links":0,"syntology":null},{"paper":null,"slug":"gradient-based-wang-landau-algorithm-a-novel","title":"Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space","date":"2023-02-19","arxiv_id":"2302.09484","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-pointrend-improved-semiconductor-wafer","title":"SEMI-PointRend: Improved Semiconductor Wafer Defect Classification and Segmentation as Rendering","date":"2023-02-19","arxiv_id":"2302.09569","n_code_links":0,"syntology":null},{"paper":null,"slug":"table-tennis-stroke-detection-and-recognition","title":"Table Tennis Stroke Detection and Recognition Using Ball Trajectory Data","date":"2023-02-19","arxiv_id":"2302.09657","n_code_links":0,"syntology":null},{"paper":"/paper/meta-style-adversarial-training-for-cross","slug":"meta-style-adversarial-training-for-cross","title":"StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning","date":"2023-02-18","arxiv_id":"2302.09309","n_code_links":2,"syntology":null},{"paper":null,"slug":"automated-lesion-segmentation-in-whole-body","title":"Automated Lesion Segmentation in Whole-Body FDG-PET/CT with Multi-modality Deep Neural Networks","date":"2023-02-16","arxiv_id":"2302.12774","n_code_links":0,"syntology":null},{"paper":"/paper/qarv-quantization-aware-resnet-vae-for-lossy","slug":"qarv-quantization-aware-resnet-vae-for-lossy","title":"QARV: Quantization-Aware ResNet VAE for Lossy Image Compression","date":"2023-02-16","arxiv_id":"2302.08899","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["gitlab.com/viper-purdue/qarv-release"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["named_in_paper"]}}},{"paper":"/paper/singular-value-representation-a-new-graph","slug":"singular-value-representation-a-new-graph","title":"Singular Value Representation: A New Graph Perspective On Neural Networks","date":"2023-02-16","arxiv_id":"2302.08183","n_code_links":1,"syntology":null},{"paper":null,"slug":"unsupervised-domain-adaptation-for-mri-volume","title":"Unsupervised Domain Adaptation for MRI Volume Segmentation and Classification Using Image-to-Image Translation","date":"2023-02-16","arxiv_id":"2302.08016","n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-pipeline-for-accurate-retinal-layer","title":"AI pipeline for accurate retinal layer segmentation using OCT 3D images","date":"2023-02-15","arxiv_id":"2302.07806","n_code_links":0,"syntology":null},{"paper":null,"slug":"cdpmsr-conditional-diffusion-probabilistic","title":"CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution","date":"2023-02-14","arxiv_id":"2302.12831","n_code_links":0,"syntology":null},{"paper":"/paper/cholectriplet2022-show-me-a-tool-and-tell-me","slug":"cholectriplet2022-show-me-a-tool-and-tell-me","title":"CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection","date":"2023-02-13","arxiv_id":"2302.06294","n_code_links":2,"syntology":null},{"paper":null,"slug":"detection-and-segmentation-of-pancreas-using","title":"Detection and Segmentation of Pancreas using Morphological Snakes and Deep Convolutional Neural Networks","date":"2023-02-13","arxiv_id":"2302.06356","n_code_links":0,"syntology":null},{"paper":"/paper/implications-of-the-convergence-of-language","slug":"implications-of-the-convergence-of-language","title":"Do Vision and Language Models Share Concepts? A Vector Space Alignment Study","date":"2023-02-13","arxiv_id":"2302.06555","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 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) · 0 unverified","official":{"repos":["jiaangli/vlca"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/learning-based-defect-recognitions-for","slug":"learning-based-defect-recognitions-for","title":"Learning-Based Defect Recognitions for Autonomous UAV Inspections","date":"2023-02-13","arxiv_id":"2302.06093","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-pseudo-colorizing-of-masked","slug":"self-supervised-pseudo-colorizing-of-masked","title":"Self-supervised pseudo-colorizing of masked cells","date":"2023-02-12","arxiv_id":"2302.05968","n_code_links":2,"syntology":null},{"paper":null,"slug":"a-ct-based-deep-learning-system-for-automatic","title":"A CT-based deep learning system for automatic assessment of aortic root morphology for TAVI planning","date":"2023-02-10","arxiv_id":"2302.05378","n_code_links":0,"syntology":null},{"paper":"/paper/evaluation-of-data-augmentation-and-loss","slug":"evaluation-of-data-augmentation-and-loss","title":"Evaluation of Data Augmentation and Loss Functions in Semantic Image Segmentation for Drilling Tool Wear Detection","date":"2023-02-10","arxiv_id":"2302.05262","n_code_links":1,"syntology":null},{"paper":null,"slug":"lithium-metal-battery-quality-control-via","title":"Lithium Metal Battery Quality Control via Transformer-CNN Segmentation","date":"2023-02-09","arxiv_id":"2302.04824","n_code_links":0,"syntology":null},{"paper":"/paper/to-perceive-or-not-to-perceive-lightweight","slug":"to-perceive-or-not-to-perceive-lightweight","title":"To Perceive or Not to Perceive: Lightweight Stacked Hourglass Network","date":"2023-02-09","arxiv_id":"2302.04815","n_code_links":1,"syntology":null},{"paper":"/paper/a-weighted-normalized-boundary-loss-for","slug":"a-weighted-normalized-boundary-loss-for","title":"A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation","date":"2023-02-08","arxiv_id":"2302.03868","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-efficient-instance-segmentation-approach","title":"An Efficient Instance Segmentation Approach for Extracting Fission Gas Bubbles on U-10Zr Annular Fuel","date":"2023-02-08","arxiv_id":"2302.12833","n_code_links":0,"syntology":null},{"paper":null,"slug":"short-term-memory-convolutions","title":"Short-Term Memory Convolutions","date":"2023-02-08","arxiv_id":"2302.04331","n_code_links":0,"syntology":null},{"paper":null,"slug":"swincross-cross-modal-swin-transformer-for","title":"SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images","date":"2023-02-08","arxiv_id":"2302.03861","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-learning-based-in-silico-framework-for","title":"A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation","date":"2023-02-07","arxiv_id":"2302.03570","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-end-to-end-two-phase-deep-learning-based","title":"An End-to-End Two-Phase Deep Learning-Based workflow to Segment Man-made Objects Around Reservoirs","date":"2023-02-07","arxiv_id":"2302.03282","n_code_links":0,"syntology":null},{"paper":"/paper/boosting-zero-shot-classification-with","slug":"boosting-zero-shot-classification-with","title":"Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion","date":"2023-02-07","arxiv_id":"2302.03298","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jordan-hs/diversity_is_definitely_needed"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"improving-ct-image-segmentation-accuracy","title":"Improving CT Image Segmentation Accuracy Using StyleGAN Driven Data Augmentation","date":"2023-02-07","arxiv_id":"2302.03285","n_code_links":0,"syntology":null},{"paper":null,"slug":"lut-nn-towards-unified-neural-network","title":"LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup","date":"2023-02-07","arxiv_id":"2302.03213","n_code_links":0,"syntology":null},{"paper":null,"slug":"vertxnet-an-ensemble-method-for-vertebrae","title":"VertXNet: An Ensemble Method for Vertebrae Segmentation and Identification of Spinal X-Ray","date":"2023-02-07","arxiv_id":"2302.03476","n_code_links":0,"syntology":null},{"paper":"/paper/amd-hooknet-for-glacier-front-segmentation","slug":"amd-hooknet-for-glacier-front-segmentation","title":"AMD-HookNet for Glacier Front Segmentation","date":"2023-02-06","arxiv_id":"2302.02744","n_code_links":1,"syntology":null},{"paper":"/paper/exploiting-partial-common-information","slug":"exploiting-partial-common-information","title":"Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation","date":"2023-02-06","arxiv_id":"2302.02521","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ysmei97/multimodal_pci_mask"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"active-learning-in-brain-tumor-segmentation","title":"Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization","date":"2023-02-05","arxiv_id":"2302.10185","n_code_links":0,"syntology":null},{"paper":null,"slug":"hyphen-a-hybrid-packing-method-and","title":"HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks","date":"2023-02-05","arxiv_id":"2302.02407","n_code_links":0,"syntology":null}],"record_sha256":"422b9b70f734bc343da455c1f485418cdf4cd2d1a4134809fa927c2d41d337c1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}