{"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/29","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":29,"pages_in_order":72,"rows_per_page":100,"rows":[2801,2900],"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/28","next":"/method/max-pooling/papers/30","papers":[{"paper":null,"slug":"left-ventricle-contouring-of-apical-three","title":"Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography","date":"2022-07-13","arxiv_id":"2207.06330","n_code_links":0,"syntology":null},{"paper":null,"slug":"yolo2u-net-detection-guided-3d-instance","title":"YOLO2U-Net: Detection-Guided 3D Instance Segmentation for Microscopy","date":"2022-07-13","arxiv_id":"2207.06215","n_code_links":0,"syntology":null},{"paper":"/paper/m-fuse-multi-frame-fusion-for-scene-flow","slug":"m-fuse-multi-frame-fusion-for-scene-flow","title":"M-FUSE: Multi-frame Fusion for Scene Flow Estimation","date":"2022-07-12","arxiv_id":"2207.05704","n_code_links":1,"syntology":null},{"paper":"/paper/next-vit-next-generation-vision-transformer","slug":"next-vit-next-generation-vision-transformer","title":"Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios","date":"2022-07-12","arxiv_id":"2207.05501","n_code_links":5,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":0,"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) · 1 unverified","official":{"repos":["bytedance/next-vit"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"vertxnet-automatic-segmentation-and","title":"VertXNet: Automatic Segmentation and Identification of Lumbar and Cervical Vertebrae from Spinal X-ray Images","date":"2022-07-12","arxiv_id":"2207.05476","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-mean-dimension-of-neural-networks-what","title":"The Mean Dimension of Neural Networks -- What causes the interaction effects?","date":"2022-07-11","arxiv_id":"2207.04890","n_code_links":0,"syntology":null},{"paper":null,"slug":"rank-enhanced-low-dimensional-convolution-set","title":"Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising","date":"2022-07-09","arxiv_id":"2207.04266","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-power-of-transfer-learning-in","title":"The Power of Transfer Learning in Agricultural Applications: AgriNet","date":"2022-07-08","arxiv_id":"2207.03881","n_code_links":0,"syntology":null},{"paper":"/paper/tfcns-a-cnn-transformer-hybrid-network-for","slug":"tfcns-a-cnn-transformer-hybrid-network-for","title":"TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation","date":"2022-07-07","arxiv_id":"2207.03450","n_code_links":1,"syntology":null},{"paper":"/paper/is-the-u-net-directional-relationship-aware","slug":"is-the-u-net-directional-relationship-aware","title":"Is the U-Net Directional-Relationship Aware?","date":"2022-07-06","arxiv_id":"2207.02574","n_code_links":1,"syntology":null},{"paper":null,"slug":"perfusion-imaging-in-deep-prostate-cancer","title":"Perfusion imaging in deep prostate cancer detection from mp-MRI: can we take advantage of it?","date":"2022-07-06","arxiv_id":"2207.02854","n_code_links":0,"syntology":null},{"paper":"/paper/swin-deformable-attention-u-net-transformer","slug":"swin-deformable-attention-u-net-transformer","title":"Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI","date":"2022-07-05","arxiv_id":"2207.02390","n_code_links":1,"syntology":null},{"paper":null,"slug":"attention-mechanisms-for-physiological-signal","title":"Attention mechanisms for physiological signal deep learning: which attention should we take?","date":"2022-07-04","arxiv_id":"2207.06904","n_code_links":0,"syntology":null},{"paper":null,"slug":"game-state-learning-via-game-scene","title":"Game State Learning via Game Scene Augmentation","date":"2022-07-04","arxiv_id":"2207.01289","n_code_links":0,"syntology":null},{"paper":null,"slug":"interpretable-fusion-analytics-framework-for","title":"Interpretable Fusion Analytics Framework for fMRI Connectivity: Self-Attention Mechanism and Latent Space Item-Response Model","date":"2022-07-04","arxiv_id":"2207.01581","n_code_links":0,"syntology":null},{"paper":null,"slug":"positive-negative-equal-contrastive-loss-for","title":"Positive-Negative Equal Contrastive Loss for Semantic Segmentation","date":"2022-07-04","arxiv_id":"2207.01417","n_code_links":0,"syntology":null},{"paper":null,"slug":"real-time-egocentric-segmentation-for-video","title":"Real Time Egocentric Segmentation for Video-self Avatar in Mixed Reality","date":"2022-07-04","arxiv_id":"2207.01296","n_code_links":0,"syntology":null},{"paper":null,"slug":"solving-learn-to-race-autonomous-racing","title":"Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space","date":"2022-07-04","arxiv_id":"2207.01275","n_code_links":0,"syntology":null},{"paper":null,"slug":"vehicle-trajectory-prediction-on-highways","title":"Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning","date":"2022-07-04","arxiv_id":"2207.01407","n_code_links":0,"syntology":null},{"paper":"/paper/generating-gender-ambiguous-voices-for","slug":"generating-gender-ambiguous-voices-for","title":"Generating gender-ambiguous voices for privacy-preserving speech recognition","date":"2022-07-03","arxiv_id":"2207.01052","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-modern-deep-learning-approaches-for","title":"On the modern deep learning approaches for precipitation downscaling","date":"2022-07-02","arxiv_id":"2207.00808","n_code_links":0,"syntology":null},{"paper":"/paper/sequence-aware-multimodal-page-classification","slug":"sequence-aware-multimodal-page-classification","title":"Sequence-aware multimodal page classification of Brazilian legal documents","date":"2022-07-02","arxiv_id":"2207.00748","n_code_links":1,"syntology":null},{"paper":"/paper/covid-19-detection-using-transfer-learning-1","slug":"covid-19-detection-using-transfer-learning-1","title":"COVID-19 Detection Using Transfer Learning Approach from Computed Tomography Images","date":"2022-07-01","arxiv_id":"2207.00259","n_code_links":1,"syntology":null},{"paper":"/paper/dissecting-self-supervised-learning-methods","slug":"dissecting-self-supervised-learning-methods","title":"Dissecting Self-Supervised Learning Methods for Surgical Computer Vision","date":"2022-07-01","arxiv_id":"2207.00449","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-segment-prostate-cancer-by","title":"Learning to segment prostate cancer by aggressiveness from scribbles in bi-parametric MRI","date":"2022-07-01","arxiv_id":"2207.05056","n_code_links":0,"syntology":null},{"paper":"/paper/color-aware-two-branch-dcnn-for-efficient","slug":"color-aware-two-branch-dcnn-for-efficient","title":"Color-aware two-branch DCNN for efficient plant disease classification","date":"2022-06-30","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/grouped-pointwise-convolutions-reduce","slug":"grouped-pointwise-convolutions-reduce","title":"Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks","date":"2022-06-30","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"implicit-u-net-for-volumetric-medical-image","title":"Implicit U-Net for volumetric medical image segmentation","date":"2022-06-30","arxiv_id":"2206.15217","n_code_links":0,"syntology":null},{"paper":"/paper/semantic-image-synthesis-via-diffusion-models","slug":"semantic-image-synthesis-via-diffusion-models","title":"Semantic Image Synthesis via Diffusion Models","date":"2022-06-30","arxiv_id":"2207.00050","n_code_links":4,"syntology":{"ran":28,"of":32,"n_ran_checked":23,"n_instrument":5,"unverified":4,"pointer_only":10,"phrase":"28 ran (of which 0 constructed an object rather than computing a result; 23 with no instrument failure: 2 honoured, 1 violated, 20 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","official":{"repos":["weilunwang/semantic-diffusion-model"],"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":["listed","official"]}}},{"paper":null,"slug":"sparse-periodic-systolic-dataflow-for","title":"Sparse Periodic Systolic Dataflow for Lowering Latency and Power Dissipation of Convolutional Neural Network Accelerators","date":"2022-06-30","arxiv_id":"2207.00068","n_code_links":0,"syntology":null},{"paper":null,"slug":"cut-inner-layers-a-structured-pruning","title":"Cut Inner Layers: A Structured Pruning Strategy for Efficient U-Net GANs","date":"2022-06-29","arxiv_id":"2206.14658","n_code_links":0,"syntology":null},{"paper":null,"slug":"ensemble-cnn-models-for-covid-19-recognition","title":"Ensemble CNN models for Covid-19 Recognition and Severity Perdition From 3D CT-scan","date":"2022-06-29","arxiv_id":"2206.15431","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-neural-networks-pruning-via-the","title":"Deep Neural Networks pruning via the Structured Perspective Regularization","date":"2022-06-28","arxiv_id":"2206.14056","n_code_links":0,"syntology":null},{"paper":null,"slug":"gan-based-super-resolution-and-segmentation","title":"GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans","date":"2022-06-28","arxiv_id":"2206.13740","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-disease-classification-performance","title":"Improving Disease Classification Performance and Explainability of Deep Learning Models in Radiology with Heatmap Generators","date":"2022-06-28","arxiv_id":"2207.00157","n_code_links":0,"syntology":null},{"paper":"/paper/robustifying-vision-transformer-without","slug":"robustifying-vision-transformer-without","title":"Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment","date":"2022-06-28","arxiv_id":"2206.13951","n_code_links":1,"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":["kojima-takeshi188/cfa"],"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"]}}},{"paper":null,"slug":"studying-generalization-through-data","title":"Studying Generalization Through Data Averaging","date":"2022-06-28","arxiv_id":"2206.13669","n_code_links":0,"syntology":null},{"paper":null,"slug":"autoinit-automatic-initialization-via","title":"AutoInit: Automatic Initialization via Jacobian Tuning","date":"2022-06-27","arxiv_id":"2206.13568","n_code_links":0,"syntology":null},{"paper":"/paper/benchopt-reproducible-efficient-and","slug":"benchopt-reproducible-efficient-and","title":"Benchopt: Reproducible, efficient and collaborative optimization benchmarks","date":"2022-06-27","arxiv_id":"2206.13424","n_code_links":3,"syntology":{"ran":6,"of":10,"n_ran_checked":6,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"6 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; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["benchopt/benchopt"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/extended-u-net-for-speaker-verification-in","slug":"extended-u-net-for-speaker-verification-in","title":"Extended U-Net for Speaker Verification in Noisy Environments","date":"2022-06-27","arxiv_id":"2206.13044","n_code_links":1,"syntology":null},{"paper":null,"slug":"knowledge-distillation-with-representative","title":"Representative Teacher Keys for Knowledge Distillation Model Compression Based on Attention Mechanism for Image Classification","date":"2022-06-26","arxiv_id":"2206.12788","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-extraction-of-coronary-arteries","title":"Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms","date":"2022-06-24","arxiv_id":"2206.12300","n_code_links":0,"syntology":null},{"paper":null,"slug":"contrastive-learning-of-features-between","title":"Contrastive Learning of Features between Images and LiDAR","date":"2022-06-24","arxiv_id":"2206.12071","n_code_links":0,"syntology":null},{"paper":"/paper/design-and-analysis-of-novel-bit-flip-attacks","slug":"design-and-analysis-of-novel-bit-flip-attacks","title":"Design and Analysis of Novel Bit-flip Attacks and Defense Strategies for DNNs","date":"2022-06-24","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/icos-protein-expression-segmentation-can","slug":"icos-protein-expression-segmentation-can","title":"ICOS Protein Expression Segmentation: Can Transformer Networks Give Better Results?","date":"2022-06-23","arxiv_id":"2206.11520","n_code_links":1,"syntology":null},{"paper":"/paper/toward-clinically-assisted-colorectal-polyp","slug":"toward-clinically-assisted-colorectal-polyp","title":"Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation Consistency","date":"2022-06-23","arxiv_id":"2206.11826","n_code_links":1,"syntology":null},{"paper":null,"slug":"cnn-based-fully-automatic-wrist-cartilage","title":"CNN-based fully automatic wrist cartilage volume quantification in MR Image","date":"2022-06-22","arxiv_id":"2206.11127","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-networks-as-paths-through-the-space-of","title":"Neural Networks as Paths through the Space of Representations","date":"2022-06-22","arxiv_id":"2206.10999","n_code_links":0,"syntology":null},{"paper":"/paper/envpool-a-highly-parallel-reinforcement","slug":"envpool-a-highly-parallel-reinforcement","title":"EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine","date":"2022-06-21","arxiv_id":"2206.10558","n_code_links":3,"syntology":{"ran":5,"of":5,"n_ran_checked":4,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["sail-sg/envpool"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["named_in_paper","official"]}}},{"paper":null,"slug":"floor-map-reconstruction-through-radio","title":"Floor Map Reconstruction Through Radio Sensing and Learning By a Large Intelligent Surface","date":"2022-06-21","arxiv_id":"2206.10750","n_code_links":0,"syntology":null},{"paper":null,"slug":"mestereo-du2cnn-a-novel-dual-channel-cnn-for","title":"MEStereo-Du2CNN: A Novel Dual Channel CNN for Learning Robust Depth Estimates from Multi-exposure Stereo Images for HDR 3D Applications","date":"2022-06-21","arxiv_id":"2206.10375","n_code_links":0,"syntology":null},{"paper":null,"slug":"sqsgd-locally-private-and-communication","title":"sqSGD: Locally Private and Communication Efficient Federated Learning","date":"2022-06-21","arxiv_id":"2206.10565","n_code_links":0,"syntology":null},{"paper":"/paper/using-the-polar-transform-for-efficient-deep","slug":"using-the-polar-transform-for-efficient-deep","title":"Using the Polar Transform for Efficient Deep Learning-Based Aorta Segmentation in CTA Images","date":"2022-06-21","arxiv_id":"2206.10294","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-machine-learning-data-fusion-model-for-soil","title":"A Machine Learning Data Fusion Model for Soil Moisture Retrieval","date":"2022-06-20","arxiv_id":"2206.09649","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-defect-classification-and","title":"Deep Learning-Based Defect Classification and Detection in SEM Images","date":"2022-06-20","arxiv_id":"2206.13505","n_code_links":0,"syntology":null},{"paper":"/paper/msanet-multi-similarity-and-attention-1","slug":"msanet-multi-similarity-and-attention-1","title":"MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentation","date":"2022-06-20","arxiv_id":"2206.09667","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"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":["AIVResearch/MSANet"],"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":["unlocated"]}}},{"paper":"/paper/visualizing-and-understanding-self-supervised","slug":"visualizing-and-understanding-self-supervised","title":"Visualizing and Understanding Contrastive Learning","date":"2022-06-20","arxiv_id":"2206.09753","n_code_links":1,"syntology":null},{"paper":null,"slug":"free-form-lesion-synthesis-using-a-partial","title":"Free-form Lesion Synthesis Using a Partial Convolution Generative Adversarial Network for Enhanced Deep Learning Liver Tumor Segmentation","date":"2022-06-18","arxiv_id":"2206.09065","n_code_links":0,"syntology":null},{"paper":"/paper/multistream-gaze-estimation-with-anatomical","slug":"multistream-gaze-estimation-with-anatomical","title":"Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning","date":"2022-06-18","arxiv_id":"2206.09256","n_code_links":1,"syntology":null},{"paper":"/paper/ctooth-a-fully-annotated-3d-dataset-and","slug":"ctooth-a-fully-annotated-3d-dataset-and","title":"CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images","date":"2022-06-17","arxiv_id":"2206.08778","n_code_links":1,"syntology":null},{"paper":null,"slug":"du-net-based-unsupervised-contrastive","title":"DU-Net based Unsupervised Contrastive Learning for Cancer Segmentation in Histology Images","date":"2022-06-17","arxiv_id":"2206.08791","n_code_links":0,"syntology":null},{"paper":"/paper/enhanced-bi-directional-motion-estimation-for","slug":"enhanced-bi-directional-motion-estimation-for","title":"Enhanced Bi-directional Motion Estimation for Video Frame Interpolation","date":"2022-06-17","arxiv_id":"2206.08572","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluation-of-contrastive-learning-with","title":"Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection","date":"2022-06-17","arxiv_id":"2206.08726","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-classification-of-brain-tumor-images","title":"Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network","date":"2022-06-17","arxiv_id":"2206.08543","n_code_links":0,"syntology":null},{"paper":null,"slug":"backbones-review-feature-extraction-networks","title":"Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches","date":"2022-06-16","arxiv_id":"2206.08016","n_code_links":0,"syntology":null},{"paper":"/paper/nucleus-segmentation-and-analysis-in-breast","slug":"nucleus-segmentation-and-analysis-in-breast","title":"Nucleus Segmentation and Analysis in Breast Cancer with the MIScnn Framework","date":"2022-06-16","arxiv_id":"2206.08182","n_code_links":2,"syntology":null},{"paper":null,"slug":"u-pet-mri-based-dementia-detection-with-joint","title":"U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images","date":"2022-06-16","arxiv_id":"2206.08078","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-detection-of-rice-disease-in-images","title":"Automatic Detection of Rice Disease in Images of Various Leaf Sizes","date":"2022-06-15","arxiv_id":"2206.07344","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-object-detector-ensembles-for","title":"Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams","date":"2022-06-15","arxiv_id":"2206.07580","n_code_links":0,"syntology":null},{"paper":null,"slug":"subsurface-depths-structure-maps","title":"Subsurface Depths Structure Maps Reconstruction with Generative Adversarial Networks","date":"2022-06-15","arxiv_id":"2206.07388","n_code_links":0,"syntology":null},{"paper":"/paper/towards-ml-methods-for-biodiversity-a-novel","slug":"towards-ml-methods-for-biodiversity-a-novel","title":"Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species Annotations","date":"2022-06-15","arxiv_id":"2206.07497","n_code_links":1,"syntology":null},{"paper":"/paper/asymmetric-dual-decoder-u-net-for-joint-rain","slug":"asymmetric-dual-decoder-u-net-for-joint-rain","title":"Asymmetric Dual-Decoder U-Net for Joint Rain and Haze Removal","date":"2022-06-14","arxiv_id":"2206.06803","n_code_links":1,"syntology":null},{"paper":"/paper/federated-multi-organ-segmentation-with","slug":"federated-multi-organ-segmentation-with","title":"Federated Multi-organ Segmentation with Inconsistent Labels","date":"2022-06-14","arxiv_id":"2206.07156","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-ensemble-learning-for-segmenting","title":"Deep ensemble learning for segmenting tuberculosis-consistent manifestations in chest radiographs","date":"2022-06-13","arxiv_id":"2206.06065","n_code_links":0,"syntology":null},{"paper":null,"slug":"fluorescence-angiography-classification-in","title":"Fluorescence angiography classification in colorectal surgery -- A preliminary report","date":"2022-06-13","arxiv_id":"2206.05935","n_code_links":0,"syntology":null},{"paper":"/paper/making-sense-of-dependence-efficient-black","slug":"making-sense-of-dependence-efficient-black","title":"Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure","date":"2022-06-13","arxiv_id":"2206.06219","n_code_links":1,"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":["paulnovello/hsic-attribution-method"],"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"]}}},{"paper":null,"slug":"robust-time-series-denoising-with-learnable","title":"Robust Time Series Denoising with Learnable Wavelet Packet Transform","date":"2022-06-13","arxiv_id":"2206.06126","n_code_links":0,"syntology":null},{"paper":"/paper/towards-alternative-techniques-for-improving","slug":"towards-alternative-techniques-for-improving","title":"Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations","date":"2022-06-13","arxiv_id":"2206.06496","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-models-for-automated","title":"Deep Learning Models for Automated Classification of Dog Emotional States from Facial Expressions","date":"2022-06-11","arxiv_id":"2206.05619","n_code_links":0,"syntology":null},{"paper":null,"slug":"mammodl-mammographic-breast-density","title":"MammoFL: Mammographic Breast Density Estimation using Federated Learning","date":"2022-06-11","arxiv_id":"2206.05575","n_code_links":0,"syntology":null},{"paper":"/paper/symbolic-image-detection-using-scene-and","slug":"symbolic-image-detection-using-scene-and","title":"Symbolic image detection using scene and knowledge graphs","date":"2022-06-10","arxiv_id":"2206.04863","n_code_links":1,"syntology":null},{"paper":"/paper/cross-modal-local-shortest-path-and-global","slug":"cross-modal-local-shortest-path-and-global","title":"Cross-modal Local Shortest Path and Global Enhancement for Visible-Thermal Person Re-Identification","date":"2022-06-09","arxiv_id":"2206.04401","n_code_links":0,"syntology":null},{"paper":null,"slug":"reface-real-time-adversarial-attacks-on-face","title":"ReFace: Real-time Adversarial Attacks on Face Recognition Systems","date":"2022-06-09","arxiv_id":"2206.04783","n_code_links":0,"syntology":null},{"paper":null,"slug":"sdq-stochastic-differentiable-quantization","title":"SDQ: Stochastic Differentiable Quantization with Mixed Precision","date":"2022-06-09","arxiv_id":"2206.04459","n_code_links":0,"syntology":null},{"paper":null,"slug":"stem-image-analysis-based-on-deep-learning","title":"STEM image analysis based on deep learning: identification of vacancy defects and polymorphs of ${MoS_2}$","date":"2022-06-09","arxiv_id":"2206.04272","n_code_links":0,"syntology":null},{"paper":"/paper/blind-face-restoration-benchmark-datasets-and","slug":"blind-face-restoration-benchmark-datasets-and","title":"Blind Face Restoration: Benchmark Datasets and a Baseline Model","date":"2022-06-08","arxiv_id":"2206.03697","n_code_links":2,"syntology":null},{"paper":"/paper/robust-deep-ensemble-method-for-real-world","slug":"robust-deep-ensemble-method-for-real-world","title":"Robust Deep Ensemble Method for Real-world Image Denoising","date":"2022-06-08","arxiv_id":"2206.03691","n_code_links":1,"syntology":null},{"paper":"/paper/hmrnet-high-and-multi-resolution-network-with","slug":"hmrnet-high-and-multi-resolution-network-with","title":"HMRNet: High and Multi-Resolution Network with Bidirectional Feature Calibration for Brain Structure Segmentation in Radiotherapy","date":"2022-06-07","arxiv_id":"2206.02959","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-resource-efficient-spiking-neural-network","title":"A Resource-efficient Spiking Neural Network Accelerator Supporting Emerging Neural Encoding","date":"2022-06-06","arxiv_id":"2206.02495","n_code_links":0,"syntology":null},{"paper":null,"slug":"implementation-of-a-modified-u-net-for","title":"Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices","date":"2022-06-06","arxiv_id":"2206.02358","n_code_links":0,"syntology":null},{"paper":"/paper/norppa-novel-ringed-seal-re-identification-by","slug":"norppa-novel-ringed-seal-re-identification-by","title":"NORPPA: NOvel Ringed seal re-identification by Pelage Pattern Aggregation","date":"2022-06-06","arxiv_id":"2206.02498","n_code_links":1,"syntology":null},{"paper":"/paper/sports-re-id-improving-re-identification-of","slug":"sports-re-id-improving-re-identification-of","title":"Sports Re-ID: Improving Re-Identification Of Players In Broadcast Videos Of Team Sports","date":"2022-06-06","arxiv_id":"2206.02373","n_code_links":1,"syntology":null},{"paper":"/paper/why-do-cnns-learn-consistent-representations","slug":"why-do-cnns-learn-consistent-representations","title":"What do CNNs Learn in the First Layer and Why? A Linear Systems Perspective","date":"2022-06-06","arxiv_id":"2206.02454","n_code_links":1,"syntology":null},{"paper":null,"slug":"sampling-frequency-independent-dialogue","title":"Sampling Frequency Independent Dialogue Separation","date":"2022-06-05","arxiv_id":"2206.02124","n_code_links":0,"syntology":null},{"paper":null,"slug":"searching-similarity-measure-for-binarized","title":"Searching Similarity Measure for Binarized Neural Networks","date":"2022-06-05","arxiv_id":"2206.03325","n_code_links":0,"syntology":null},{"paper":null,"slug":"cainnflow-convolutional-block-attention","title":"CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks","date":"2022-06-04","arxiv_id":"2206.01992","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-learning-usability-expansion-model-of","title":"A Deep-Learning Usability Expansion Model of Ocean Observations","date":"2022-06-03","arxiv_id":"2206.01599","n_code_links":0,"syntology":null},{"paper":"/paper/contrastive-learning-unifies-t-sne-and-umap","slug":"contrastive-learning-unifies-t-sne-and-umap","title":"From $t$-SNE to UMAP with contrastive learning","date":"2022-06-03","arxiv_id":"2206.01816","n_code_links":3,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 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; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["hci-unihd/cl-tsne-umap","berenslab/contrastive-ne"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"eaanet-efficient-attention-augmented","title":"EAANet: Efficient Attention Augmented Convolutional Networks","date":"2022-06-03","arxiv_id":"2206.01821","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-duality-between-contrastive-and-non","title":"On the duality between contrastive and non-contrastive self-supervised learning","date":"2022-06-03","arxiv_id":"2206.02574","n_code_links":0,"syntology":null}],"record_sha256":"aa3a3a64152423721e64a99310ca23c743bbdbcf3f49dc006e0afd15e1d3d717","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}