{"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/21","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":21,"pages_in_order":72,"rows_per_page":100,"rows":[2001,2100],"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/20","next":"/method/max-pooling/papers/22","papers":[{"paper":"/paper/deepscribe-localization-and-classification-of","slug":"deepscribe-localization-and-classification-of","title":"DeepScribe: Localization and Classification of Elamite Cuneiform Signs Via Deep Learning","date":"2023-06-02","arxiv_id":"2306.01268","n_code_links":1,"syntology":null},{"paper":null,"slug":"erfrelu-adaptive-activation-function-for-deep","title":"ErfReLU: Adaptive Activation Function for Deep Neural Network","date":"2023-06-02","arxiv_id":"2306.01822","n_code_links":0,"syntology":null},{"paper":"/paper/group-channel-pruning-and-spatial-attention","slug":"group-channel-pruning-and-spatial-attention","title":"Group channel pruning and spatial attention distilling for object detection","date":"2023-06-02","arxiv_id":"2306.01526","n_code_links":1,"syntology":null},{"paper":null,"slug":"hd-demucs-general-speech-restoration-with","title":"HD-DEMUCS: General Speech Restoration with Heterogeneous Decoders","date":"2023-06-02","arxiv_id":"2306.01411","n_code_links":0,"syntology":null},{"paper":null,"slug":"sub-meter-tree-height-mapping-of-california","title":"Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model","date":"2023-06-02","arxiv_id":"2306.01936","n_code_links":0,"syntology":null},{"paper":"/paper/a-multi-dimensional-deep-structured-state","slug":"a-multi-dimensional-deep-structured-state","title":"A Multi-dimensional Deep Structured State Space Approach to Speech Enhancement Using Small-footprint Models","date":"2023-06-01","arxiv_id":"2306.00331","n_code_links":1,"syntology":null},{"paper":null,"slug":"autism-disease-detection-using-transfer","title":"Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison Between Central Processing Unit vs Graphics Processing Unit Functions for Neural Networks","date":"2023-06-01","arxiv_id":"2306.00283","n_code_links":0,"syntology":null},{"paper":"/paper/stablerep-synthetic-images-from-text-to-image","slug":"stablerep-synthetic-images-from-text-to-image","title":"StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners","date":"2023-06-01","arxiv_id":"2306.00984","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"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":["google-research/syn-rep-learn"],"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":"/paper/a-unified-framework-for-u-net-design-and-1","slug":"a-unified-framework-for-u-net-design-and-1","title":"A Unified Framework for U-Net Design and Analysis","date":"2023-05-31","arxiv_id":"2305.19638","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":6,"n_instrument":1,"unverified":1,"pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["fabianfalck/unet-design"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/augmentation-aware-self-supervised-learning","slug":"augmentation-aware-self-supervised-learning","title":"Augmentation-aware Self-supervised Learning with Conditioned Projector","date":"2023-05-31","arxiv_id":"2306.06082","n_code_links":1,"syntology":{"ran":4,"of":9,"n_ran_checked":0,"n_instrument":4,"unverified":5,"pointer_only":9,"phrase":"4 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; 4 where Syntology's instrument failed) · 5 unverified","official":{"repos":["gmum/cassle"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/few-shot-class-incremental-audio-1","slug":"few-shot-class-incremental-audio-1","title":"Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes","date":"2023-05-31","arxiv_id":"2305.19539","n_code_links":1,"syntology":null},{"paper":null,"slug":"morphological-classification-of-radio-1","title":"Morphological Classification of Radio Galaxies using Semi-Supervised Group Equivariant CNNs","date":"2023-05-31","arxiv_id":"2306.00031","n_code_links":0,"syntology":null},{"paper":"/paper/the-canadian-cropland-dataset-a-new-land","slug":"the-canadian-cropland-dataset-a-new-land","title":"The Canadian Cropland Dataset: A New Land Cover Dataset for Multitemporal Deep Learning Classification in Agriculture","date":"2023-05-31","arxiv_id":"2306.00114","n_code_links":1,"syntology":null},{"paper":"/paper/exploring-the-promise-and-limits-of-real-time","slug":"exploring-the-promise-and-limits-of-real-time","title":"Exploring the Promise and Limits of Real-Time Recurrent Learning","date":"2023-05-30","arxiv_id":"2305.19044","n_code_links":1,"syntology":null},{"paper":null,"slug":"few-shot-classification-with-shrinkage","title":"SENet: A Spectral Filtering Approach to Represent Exemplars for Few-shot Learning","date":"2023-05-30","arxiv_id":"2305.18970","n_code_links":0,"syntology":null},{"paper":null,"slug":"trustworthy-sensor-fusion-against-inaudible","title":"Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance System","date":"2023-05-30","arxiv_id":"2306.05358","n_code_links":0,"syntology":null},{"paper":null,"slug":"domo-ac-doubly-multi-step-off-policy-actor","title":"DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm","date":"2023-05-29","arxiv_id":"2305.18501","n_code_links":0,"syntology":null},{"paper":"/paper/mt-slvr-multi-task-self-supervised-learning","slug":"mt-slvr-multi-task-self-supervised-learning","title":"MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In(Variant) Representations","date":"2023-05-29","arxiv_id":"2305.17191","n_code_links":1,"syntology":null},{"paper":"/paper/kernel-ssl-kernel-kl-divergence-for-self","slug":"kernel-ssl-kernel-kl-divergence-for-self","title":"Matrix Information Theory for Self-Supervised Learning","date":"2023-05-27","arxiv_id":"2305.17326","n_code_links":3,"syntology":{"ran":5,"of":5,"n_ran_checked":1,"n_instrument":4,"unverified":0,"pointer_only":5,"phrase":"5 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; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yifanzhang-pro/matrix-llm","yifanzhang-pro/matrix-ssl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"deepseanet-improving-underwater-object","title":"DeepSeaNet: Improving Underwater Object Detection using EfficientDet","date":"2023-05-26","arxiv_id":"2306.06075","n_code_links":0,"syntology":null},{"paper":"/paper/dual-bayesian-resnet-a-deep-learning-approach-1","slug":"dual-bayesian-resnet-a-deep-learning-approach-1","title":"Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection","date":"2023-05-26","arxiv_id":"2305.16691","n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-modeling-of-magnetic-tape-recorders","title":"Neural modeling of magnetic tape recorders","date":"2023-05-26","arxiv_id":"2305.16862","n_code_links":0,"syntology":null},{"paper":null,"slug":"theoretical-and-practical-perspectives-on","title":"Theoretical and Practical Perspectives on what Influence Functions Do","date":"2023-05-26","arxiv_id":"2305.16971","n_code_links":0,"syntology":null},{"paper":"/paper/multilingual-text-to-speech-synthesis-for","slug":"multilingual-text-to-speech-synthesis-for","title":"Multilingual Text-to-Speech Synthesis for Turkic Languages Using Transliteration","date":"2023-05-25","arxiv_id":"2305.15749","n_code_links":1,"syntology":null},{"paper":null,"slug":"umat-uncertainty-aware-single-image-high-1","title":"UMat: Uncertainty-Aware Single Image High Resolution Material Capture","date":"2023-05-25","arxiv_id":"2305.16312","n_code_links":0,"syntology":null},{"paper":"/paper/a-neural-space-time-representation-for-text","slug":"a-neural-space-time-representation-for-text","title":"A Neural Space-Time Representation for Text-to-Image Personalization","date":"2023-05-24","arxiv_id":"2305.15391","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-entire-renal-anatomy-extraction-network","title":"An Entire Renal Anatomy Extraction Network for Advanced CAD During Partial Nephrectomy","date":"2023-05-23","arxiv_id":"2305.13616","n_code_links":0,"syntology":null},{"paper":null,"slug":"sorted-convolutional-network-for-achieving","title":"Sorted Convolutional Network for Achieving Continuous Rotational Invariance","date":"2023-05-23","arxiv_id":"2305.14462","n_code_links":0,"syntology":null},{"paper":null,"slug":"supervised-multi-regional-segmentation","title":"Supervised Multi-Regional Segmentation Machine Learning Architecture for Digital Twin Applications in Coastal Regions","date":"2023-05-23","arxiv_id":"2305.14460","n_code_links":0,"syntology":null},{"paper":null,"slug":"2305-14383","title":"A Rational Model of Dimension-reduced Human Categorization","date":"2023-05-22","arxiv_id":"2305.14383","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-efficient-deep-learning-model-to","title":"An Optimized Ensemble Deep Learning Model For Brain Tumor Classification","date":"2023-05-22","arxiv_id":"2305.12844","n_code_links":0,"syntology":null},{"paper":null,"slug":"instructvid2vid-controllable-video-editing","title":"InstructVid2Vid: Controllable Video Editing with Natural Language Instructions","date":"2023-05-21","arxiv_id":"2305.12328","n_code_links":0,"syntology":null},{"paper":null,"slug":"human-labeling-errors-and-their-impact-on","title":"Human-annotated label noise and their impact on ConvNets for remote sensing image scene classification","date":"2023-05-20","arxiv_id":"2305.12106","n_code_links":0,"syntology":null},{"paper":null,"slug":"magic-multi-modality-guided-image-completion","title":"MaGIC: Multi-modality Guided Image Completion","date":"2023-05-19","arxiv_id":"2305.11818","n_code_links":0,"syntology":null},{"paper":"/paper/not-all-semantics-are-created-equal","slug":"not-all-semantics-are-created-equal","title":"Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization","date":"2023-05-19","arxiv_id":"2305.11965","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-subabdominal-mri-image-segmentation","title":"An image segmentation algorithm based on multi-scale feature pyramid network","date":"2023-05-18","arxiv_id":"2305.10631","n_code_links":0,"syntology":null},{"paper":null,"slug":"benchmarking-deep-learning-frameworks-for","title":"Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation","date":"2023-05-18","arxiv_id":"2305.10975","n_code_links":0,"syntology":null},{"paper":null,"slug":"fastfit-towards-real-time-iterative-neural","title":"FastFit: Towards Real-Time Iterative Neural Vocoder by Replacing U-Net Encoder With Multiple STFTs","date":"2023-05-18","arxiv_id":"2305.10823","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-for-phase-resolved","title":"Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data","date":"2023-05-18","arxiv_id":"2305.11913","n_code_links":0,"syntology":null},{"paper":"/paper/sharing-lifelong-reinforcement-learning","slug":"sharing-lifelong-reinforcement-learning","title":"Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks","date":"2023-05-18","arxiv_id":"2305.10997","n_code_links":2,"syntology":null},{"paper":null,"slug":"tuned-contrastive-learning","title":"Tuned Contrastive Learning","date":"2023-05-18","arxiv_id":"2305.10675","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-aggregation-of-monte-carlo-augmented","slug":"adaptive-aggregation-of-monte-carlo-augmented","title":"Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural network","date":"2023-05-17","arxiv_id":"2305.10110","n_code_links":1,"syntology":null},{"paper":"/paper/state-representation-learning-using-an","slug":"state-representation-learning-using-an","title":"State Representation Learning Using an Unbalanced Atlas","date":"2023-05-17","arxiv_id":"2305.10267","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-relu-networks-have-surprisingly-simple","title":"Deep ReLU Networks Have Surprisingly Simple Polytopes","date":"2023-05-16","arxiv_id":"2305.09145","n_code_links":0,"syntology":null},{"paper":null,"slug":"expressnet-an-explainable-residual-slim","title":"EXPRESSNET: An Explainable Residual Slim Network for Fingerprint Presentation Attack Detection","date":"2023-05-16","arxiv_id":"2305.09397","n_code_links":0,"syntology":null},{"paper":null,"slug":"identification-and-classification-of-3","title":"Identification and Classification of Exoplanets Using Machine Learning Techniques","date":"2023-05-16","arxiv_id":"2305.09596","n_code_links":0,"syntology":null},{"paper":null,"slug":"improved-type-iii-solar-radio-burst-detection","title":"Improved Type III solar radio burst detection using congruent deep learning models","date":"2023-05-16","arxiv_id":"2305.09327","n_code_links":0,"syntology":null},{"paper":"/paper/make-an-animation-large-scale-text","slug":"make-an-animation-large-scale-text","title":"Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation","date":"2023-05-16","arxiv_id":"2305.09662","n_code_links":0,"syntology":null},{"paper":null,"slug":"out-of-distribution-detection-for-adaptive","title":"Out-of-Distribution Detection for Adaptive Computer Vision","date":"2023-05-16","arxiv_id":"2305.09293","n_code_links":0,"syntology":null},{"paper":null,"slug":"segmentation-of-aortic-vessel-tree-in-ct","title":"Segmentation of Aortic Vessel Tree in CT Scans with Deep Fully Convolutional Networks","date":"2023-05-16","arxiv_id":"2305.09833","n_code_links":0,"syntology":null},{"paper":"/paper/dragon-alpha-cu32-a-java-based-tensor","slug":"dragon-alpha-cu32-a-java-based-tensor","title":"Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library","date":"2023-05-15","arxiv_id":"2305.08819","n_code_links":1,"syntology":null},{"paper":"/paper/learning-better-contrastive-view-from","slug":"learning-better-contrastive-view-from","title":"Learning Better Contrastive View from Radiologist's Gaze","date":"2023-05-15","arxiv_id":"2305.08826","n_code_links":1,"syntology":null},{"paper":null,"slug":"straightening-out-the-straight-through","title":"Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks","date":"2023-05-15","arxiv_id":"2305.08842","n_code_links":0,"syntology":null},{"paper":"/paper/the-whole-is-greater-than-the-sum-of-its-3","slug":"the-whole-is-greater-than-the-sum-of-its-3","title":"The Whole Is Greater than the Sum of Its Parts: Improving Music Source Separation by Bridging Network","date":"2023-05-13","arxiv_id":"2305.07855","n_code_links":1,"syntology":null},{"paper":null,"slug":"rethink-depth-separation-with-intra-layer","title":"On Expressivity of Height in Neural Networks","date":"2023-05-11","arxiv_id":"2305.07037","n_code_links":0,"syntology":null},{"paper":null,"slug":"compressing-neural-network-by-tensor-network","title":"Compressing neural network by tensor network with exponentially fewer variational parameters","date":"2023-05-10","arxiv_id":"2305.06058","n_code_links":0,"syntology":null},{"paper":"/paper/hypere2vid-improving-event-based-video","slug":"hypere2vid-improving-event-based-video","title":"HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks","date":"2023-05-10","arxiv_id":"2305.06382","n_code_links":1,"syntology":null},{"paper":"/paper/low-light-image-enhancement-via-structure","slug":"low-light-image-enhancement-via-structure","title":"Low-Light Image Enhancement via Structure Modeling and Guidance","date":"2023-05-10","arxiv_id":"2305.05839","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, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["xiaogang00/smg-llie"],"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":"multiclass-mri-brain-tumor-segmentation-using","title":"Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net","date":"2023-05-10","arxiv_id":"2305.06203","n_code_links":0,"syntology":null},{"paper":"/paper/a-mountain-shaped-single-stage-network-for","slug":"a-mountain-shaped-single-stage-network-for","title":"A Mountain-Shaped Single-Stage Network for Accurate Image Restoration","date":"2023-05-09","arxiv_id":"2305.05146","n_code_links":1,"syntology":null},{"paper":null,"slug":"domain-independent-post-processing-with-graph","title":"Domain independent post-processing with graph U-nets: Applications to Electrical Impedance Tomographic Imaging","date":"2023-05-08","arxiv_id":"2305.05020","n_code_links":0,"syntology":null},{"paper":null,"slug":"rats-nas-redirection-of-adjacent-trails-on","title":"RATs-NAS: Redirection of Adjacent Trails on GCN for Neural Architecture Search","date":"2023-05-07","arxiv_id":"2305.04206","n_code_links":0,"syntology":null},{"paper":"/paper/annotation-efficient-learning-for-oct","slug":"annotation-efficient-learning-for-oct","title":"Annotation-efficient learning for OCT segmentation","date":"2023-05-06","arxiv_id":"2305.03936","n_code_links":1,"syntology":null},{"paper":null,"slug":"feature-chirality-in-deep-learning-models","title":"Feature Chirality in Deep Learning Models","date":"2023-05-06","arxiv_id":"2305.03966","n_code_links":0,"syntology":null},{"paper":"/paper/unlocking-low-light-rainy-image-restoration","slug":"unlocking-low-light-rainy-image-restoration","title":"Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the Dark","date":"2023-05-06","arxiv_id":"2305.03997","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-ensemble-of-convolution-based-methods-for","title":"An ensemble of convolution-based methods for fault detection using vibration signals","date":"2023-05-05","arxiv_id":"2305.05532","n_code_links":0,"syntology":null},{"paper":null,"slug":"heteroedge-addressing-asymmetry-in","title":"HeteroEdge: Addressing Asymmetry in Heterogeneous Collaborative Autonomous Systems","date":"2023-05-05","arxiv_id":"2305.03252","n_code_links":0,"syntology":null},{"paper":null,"slug":"breast-cancer-diagnosis-using-machine","title":"Breast Cancer Diagnosis Using Machine Learning Techniques","date":"2023-05-04","arxiv_id":"2305.02482","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-learning-for-organs-at-risk","title":"Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification","date":"2023-05-04","arxiv_id":"2305.02491","n_code_links":0,"syntology":null},{"paper":"/paper/new-adversarial-image-detection-based-on","slug":"new-adversarial-image-detection-based-on","title":"New Adversarial Image Detection Based on Sentiment Analysis","date":"2023-05-03","arxiv_id":"2305.03173","n_code_links":1,"syntology":null},{"paper":null,"slug":"fully-automatic-mitral-valve-4d-shape","title":"CNN-based fully automatic mitral valve extraction using CT images and existence probability maps","date":"2023-05-01","arxiv_id":"2305.00627","n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-tone-mapping-and-denoising-of-thermal","title":"Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning","date":"2023-05-01","arxiv_id":"2305.00691","n_code_links":0,"syntology":null},{"paper":"/paper/zerosearch-local-image-search-from-text-with","slug":"zerosearch-local-image-search-from-text-with","title":"ZeroSearch: Local Image Search from Text with Zero Shot Learning","date":"2023-05-01","arxiv_id":"2305.00715","n_code_links":1,"syntology":null},{"paper":null,"slug":"cross-shaped-windows-transformer-with-self","title":"Cross-Shaped Windows Transformer with Self-supervised Pretraining for Clinically Significant Prostate Cancer Detection in Bi-parametric MRI","date":"2023-04-30","arxiv_id":"2305.00385","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-direct-sampling-based-deep-learning","title":"A Direct Sampling-Based Deep Learning Approach for Inverse Medium Scattering Problems","date":"2023-04-29","arxiv_id":"2305.00250","n_code_links":0,"syntology":null},{"paper":null,"slug":"brain-tumor-segmentation-from-mri-images","title":"Brain Tumor Segmentation from MRI Images using Deep Learning Techniques","date":"2023-04-29","arxiv_id":"2305.00257","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-automated-end-to-end-deep-learning-based","title":"An automated end-to-end deep learning-based framework for lung cancer diagnosis by detecting and classifying the lung nodules","date":"2023-04-28","arxiv_id":"2305.00046","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-intellectual-property-a-survey","title":"Deep Intellectual Property Protection: A Survey","date":"2023-04-28","arxiv_id":"2304.14613","n_code_links":0,"syntology":null},{"paper":null,"slug":"diamant-dual-image-attention-map-encoders-for","title":"DIAMANT: Dual Image-Attention Map Encoders For Medical Image Segmentation","date":"2023-04-28","arxiv_id":"2304.14571","n_code_links":0,"syntology":null},{"paper":"/paper/deep-spatiotemporal-clustering-a-temporal","slug":"deep-spatiotemporal-clustering-a-temporal","title":"Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data","date":"2023-04-27","arxiv_id":"2304.14541","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["big-data-lab-umbc/multivariate-weather-data-clustering"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/effect-of-latent-space-distribution-on-the","slug":"effect-of-latent-space-distribution-on-the","title":"Effect of latent space distribution on the segmentation of images with multiple annotations","date":"2023-04-26","arxiv_id":"2304.13476","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploiting-cnns-for-semantic-segmentation","title":"Exploiting CNNs for Semantic Segmentation with Pascal VOC","date":"2023-04-26","arxiv_id":"2304.13216","n_code_links":0,"syntology":null},{"paper":"/paper/low-field-magnetic-resonance-image","slug":"low-field-magnetic-resonance-image","title":"Low-field magnetic resonance image enhancement via stochastic image quality transfer","date":"2023-04-26","arxiv_id":"2304.13385","n_code_links":1,"syntology":null},{"paper":null,"slug":"sensitive-tuning-of-large-scale-cnns-for-e2e","title":"Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption","date":"2023-04-26","arxiv_id":"2304.14836","n_code_links":0,"syntology":null},{"paper":"/paper/application-of-segment-anything-model-for","slug":"application-of-segment-anything-model-for","title":"Application of Segment Anything Model for Civil Infrastructure Defect Assessment","date":"2023-04-25","arxiv_id":"2304.12600","n_code_links":2,"syntology":null},{"paper":null,"slug":"objectives-matter-understanding-the-impact-of","title":"Objectives Matter: Understanding the Impact of Self-Supervised Objectives on Vision Transformer Representations","date":"2023-04-25","arxiv_id":"2304.13089","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-shared-memory-architectures-for-end","title":"Exploring shared memory architectures for end-to-end gigapixel deep learning","date":"2023-04-24","arxiv_id":"2304.12149","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-crop-contrastive-learning-for","title":"Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation","date":"2023-04-24","arxiv_id":"2304.12235","n_code_links":0,"syntology":null},{"paper":null,"slug":"now-you-see-me-robust-approach-to-partial","title":"Now You See Me: Robust approach to Partial Occlusions","date":"2023-04-24","arxiv_id":"2304.11779","n_code_links":0,"syntology":null},{"paper":"/paper/segmentation-of-hemorrhagic-areas-in-human","slug":"segmentation-of-hemorrhagic-areas-in-human","title":"Segmentation of Hemorrhagic Areas in Human Brain from CT Scan Images","date":"2023-04-24","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"topology-aware-focal-loss-for-3d-image","title":"Topology-Aware Focal Loss for 3D Image Segmentation","date":"2023-04-24","arxiv_id":"2304.12223","n_code_links":0,"syntology":null},{"paper":"/paper/dilated-unet-a-fast-and-accurate-medical","slug":"dilated-unet-a-fast-and-accurate-medical","title":"Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture","date":"2023-04-22","arxiv_id":"2304.11450","n_code_links":1,"syntology":null},{"paper":"/paper/a-preliminary-study-of-deep-learning-sensor","slug":"a-preliminary-study-of-deep-learning-sensor","title":"A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian Detection","date":"2023-04-21","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/boosting-multiple-sclerosis-lesion","slug":"boosting-multiple-sclerosis-lesion","title":"Boosting multiple sclerosis lesion segmentation through attention mechanism","date":"2023-04-21","arxiv_id":"2304.10790","n_code_links":1,"syntology":null},{"paper":"/paper/exploiting-patch-sizes-and-resolutions-for","slug":"exploiting-patch-sizes-and-resolutions-for","title":"Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification","date":"2023-04-21","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/angle-based-dynamic-learning-rate-for","slug":"angle-based-dynamic-learning-rate-for","title":"Angle based dynamic learning rate for gradient descent","date":"2023-04-20","arxiv_id":"2304.10457","n_code_links":1,"syntology":null},{"paper":null,"slug":"brain-tumor-multi-classification-and","title":"Brain tumor multi classification and segmentation in MRI images using deep learning","date":"2023-04-20","arxiv_id":"2304.10039","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-object-detection-robustness-a","title":"Enhancing object detection robustness: A synthetic and natural perturbation approach","date":"2023-04-20","arxiv_id":"2304.10622","n_code_links":0,"syntology":null},{"paper":null,"slug":"improved-active-fire-detection-using","title":"Improved Active Fire Detection using Operational U-Nets","date":"2023-04-19","arxiv_id":"2304.09721","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-urban-flood-prediction-using-lstm","title":"Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusion","date":"2023-04-19","arxiv_id":"2304.09994","n_code_links":0,"syntology":null}],"record_sha256":"4529d897058d62ea51f54965e3a50e378823d9564cd46671e8e7c42318e7a455","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}