{"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/2","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":2,"pages_in_order":72,"rows_per_page":100,"rows":[101,200],"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","next":"/method/max-pooling/papers/3","papers":[{"paper":null,"slug":"comparative-analysis-of-lightweight-deep","title":"Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices","date":"2025-05-06","arxiv_id":"2505.03303","n_code_links":0,"syntology":null},{"paper":null,"slug":"mri-motion-correction-via-efficient-residual","title":"MRI motion correction via efficient residual-guided denoising diffusion probabilistic models","date":"2025-05-06","arxiv_id":"2505.03498","n_code_links":0,"syntology":null},{"paper":"/paper/not-all-parameters-matter-masking-diffusion","slug":"not-all-parameters-matter-masking-diffusion","title":"Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability","date":"2025-05-06","arxiv_id":"2505.03097","n_code_links":1,"syntology":null},{"paper":"/paper/upmad-net-a-brain-tumor-segmentation-network","slug":"upmad-net-a-brain-tumor-segmentation-network","title":"UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion","date":"2025-05-06","arxiv_id":"2505.03494","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-proficiency-assessment-in-l2","title":"Automatic Proficiency Assessment in L2 English Learners","date":"2025-05-05","arxiv_id":"2505.02615","n_code_links":0,"syntology":null},{"paper":null,"slug":"platelet-enumeration-in-dense-aggregates","title":"Platelet enumeration in dense aggregates","date":"2025-05-05","arxiv_id":"2505.02751","n_code_links":0,"syntology":null},{"paper":null,"slug":"sharpness-aware-minimization-with-z-score","title":"Sharpness-Aware Minimization with Z-Score Gradient Filtering for Neural Networks","date":"2025-05-05","arxiv_id":"2505.02369","n_code_links":0,"syntology":null},{"paper":null,"slug":"emulator-rapid-estimation-of-complex-valued","title":"EMulator: Rapid Estimation of Complex-valued Electric Fields using a U-Net Architecture","date":"2025-05-04","arxiv_id":"2505.02095","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieval-augmented-in-context-learning-for","title":"Retrieval-augmented in-context learning for multimodal large language models in disease classification","date":"2025-05-04","arxiv_id":"2505.02087","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-robustness-of-deep-learning-1","slug":"adversarial-robustness-of-deep-learning-1","title":"Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images","date":"2025-05-03","arxiv_id":"2505.01884","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-neural-architecture-search-method-using","title":"A Neural Architecture Search Method using Auxiliary Evaluation Metric based on ResNet Architecture","date":"2025-05-02","arxiv_id":"2505.01313","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-foundation-models-really-segment-tumors-a","title":"Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging","date":"2025-05-02","arxiv_id":"2505.01239","n_code_links":0,"syntology":null},{"paper":"/paper/cav-mae-sync-improving-contrastive-audio","slug":"cav-mae-sync-improving-contrastive-audio","title":"CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment","date":"2025-05-02","arxiv_id":"2505.01237","n_code_links":1,"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: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["edsonroteia/cav-mae-sync"],"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":"/paper/aware-net-adaptive-weighted-averaging-for","slug":"aware-net-adaptive-weighted-averaging-for","title":"AWARE-NET: Adaptive Weighted Averaging for Robust Ensemble Network in Deepfake Detection","date":"2025-05-01","arxiv_id":"2505.00312","n_code_links":1,"syntology":null},{"paper":"/paper/surrogate-modeling-of-cellular-potts-agent","slug":"surrogate-modeling-of-cellular-potts-agent","title":"Surrogate modeling of Cellular-Potts Agent-Based Models as a segmentation task using the U-Net neural network architecture","date":"2025-05-01","arxiv_id":"2505.00316","n_code_links":1,"syntology":null},{"paper":null,"slug":"smart-environmental-monitoring-of-marine","title":"Smart Environmental Monitoring of Marine Pollution using Edge AI","date":"2025-04-30","arxiv_id":"2504.21759","n_code_links":0,"syntology":null},{"paper":null,"slug":"erased-but-not-forgotten-how-backdoors","title":"Erased but Not Forgotten: How Backdoors Compromise Concept Erasure","date":"2025-04-29","arxiv_id":"2504.21072","n_code_links":0,"syntology":null},{"paper":null,"slug":"mjolnir-a-deep-learning-parametrization","title":"Mjölnir: A Deep Learning Parametrization Framework for Global Lightning Flash Density","date":"2025-04-28","arxiv_id":"2504.19822","n_code_links":0,"syntology":null},{"paper":"/paper/telesparse-practical-privacy-preserving","slug":"telesparse-practical-privacy-preserving","title":"TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks","date":"2025-04-27","arxiv_id":"2504.19274","n_code_links":1,"syntology":null},{"paper":null,"slug":"global-stress-generation-and-spatiotemporal","title":"Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials","date":"2025-04-26","arxiv_id":"2505.01438","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-stress-in-two-phase-random","title":"Predicting Stress in Two-phase Random Materials and Super-Resolution Method for Stress Images by Embedding Physical Information","date":"2025-04-26","arxiv_id":"2504.18854","n_code_links":0,"syntology":null},{"paper":null,"slug":"hepatogen-generating-hepatobiliary-phase-mri","title":"HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models","date":"2025-04-25","arxiv_id":"2504.18405","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-machine-learning-approach-for-denoising-and","title":"A Machine Learning Approach for Denoising and Upsampling HRTFs","date":"2025-04-24","arxiv_id":"2504.17586","n_code_links":0,"syntology":null},{"paper":null,"slug":"aerial-image-classification-in-scarce-and","title":"Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction","date":"2025-04-24","arxiv_id":"2504.17655","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-explainable-nature-inspired-framework-for","title":"An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm","date":"2025-04-24","arxiv_id":"2504.17540","n_code_links":0,"syntology":null},{"paper":null,"slug":"masked-strategies-for-images-with-small","title":"Masked strategies for images with small objects","date":"2025-04-24","arxiv_id":"2504.17935","n_code_links":0,"syntology":null},{"paper":null,"slug":"ecgdedrdnet-a-deep-learning-based-method-for","title":"ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network","date":"2025-04-23","arxiv_id":"2505.05477","n_code_links":0,"syntology":null},{"paper":null,"slug":"representation-learning-via-non-contrastive","title":"Representation Learning via Non-Contrastive Mutual Information","date":"2025-04-23","arxiv_id":"2504.16667","n_code_links":0,"syntology":null},{"paper":null,"slug":"simplified-swarm-learning-framework-for","title":"Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology","date":"2025-04-23","arxiv_id":"2504.16732","n_code_links":0,"syntology":null},{"paper":null,"slug":"comprehensive-evaluation-of-quantitative","title":"Comprehensive Evaluation of Quantitative Measurements from Automated Deep Segmentations of PSMA PET/CT Images","date":"2025-04-22","arxiv_id":"2504.16237","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-self-supervised-learning-method-for-raman","title":"A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders","date":"2025-04-21","arxiv_id":"2504.16130","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-compositional-transferability-of","title":"Learning Compositional Transferability of Time Series for Source-Free Domain Adaptation","date":"2025-04-21","arxiv_id":"2504.14994","n_code_links":0,"syntology":null},{"paper":null,"slug":"med-2d-segnet-a-light-weight-deep-neural","title":"Med-2D SegNet: A Light Weight Deep Neural Network for Medical 2D Image Segmentation","date":"2025-04-20","arxiv_id":"2504.14715","n_code_links":0,"syntology":null},{"paper":null,"slug":"resnetvllm-2-addressing-resnetvllm-s-multi","title":"ResNetVLLM-2: Addressing ResNetVLLM's Multi-Modal Hallucinations","date":"2025-04-20","arxiv_id":"2504.14429","n_code_links":0,"syntology":null},{"paper":null,"slug":"resnetvllm-multi-modal-vision-llm-for-the","title":"ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task","date":"2025-04-20","arxiv_id":"2504.14432","n_code_links":0,"syntology":null},{"paper":null,"slug":"transforming-hyperspectral-images-into","title":"Transforming Hyperspectral Images Into Chemical Maps: An End-to-End Deep Learning Approach","date":"2025-04-19","arxiv_id":"2504.14131","n_code_links":0,"syntology":null},{"paper":null,"slug":"cardiac-mri-semantic-segmentation-for","title":"Cardiac MRI Semantic Segmentation for Ventricles and Myocardium using Deep Learning","date":"2025-04-18","arxiv_id":"2504.13391","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-parameter-adaptation-for-multi","title":"Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis","date":"2025-04-18","arxiv_id":"2504.13645","n_code_links":0,"syntology":null},{"paper":null,"slug":"fairness-and-robustness-in-machine-unlearning","title":"Fairness and Robustness in Machine Unlearning","date":"2025-04-18","arxiv_id":"2504.13610","n_code_links":0,"syntology":null},{"paper":"/paper/filter2noise-interpretable-self-supervised","slug":"filter2noise-interpretable-self-supervised","title":"Filter2Noise: Interpretable Self-Supervised Single-Image Denoising for Low-Dose CT with Attention-Guided Bilateral Filtering","date":"2025-04-18","arxiv_id":"2504.13519","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-cocoa-pod-disease-classification","title":"Enhancing Cocoa Pod Disease Classification via Transfer Learning and Ensemble Methods: Toward Robust Predictive Modeling","date":"2025-04-17","arxiv_id":"2504.12992","n_code_links":0,"syntology":null},{"paper":null,"slug":"hybrid-dense-unet201-optimization-for-pap","title":"Hybrid Dense-UNet201 Optimization for Pap Smear Image Segmentation Using Spider Monkey Optimization","date":"2025-04-17","arxiv_id":"2504.12807","n_code_links":0,"syntology":null},{"paper":"/paper/putting-the-segment-anything-model-to-the","slug":"putting-the-segment-anything-model-to-the","title":"Putting the Segment Anything Model to the Test with 3D Knee MRI - A Comparison with State-of-the-Art Performance","date":"2025-04-17","arxiv_id":"2504.13340","n_code_links":1,"syntology":null},{"paper":"/paper/gluse-enhanced-channel-wise-adaptive-gated","slug":"gluse-enhanced-channel-wise-adaptive-gated","title":"GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification","date":"2025-04-16","arxiv_id":"2504.12484","n_code_links":1,"syntology":null},{"paper":"/paper/instantcharacter-personalize-any-characters","slug":"instantcharacter-personalize-any-characters","title":"InstantCharacter: Personalize Any Characters with a Scalable Diffusion Transformer Framework","date":"2025-04-16","arxiv_id":"2504.12395","n_code_links":1,"syntology":null},{"paper":null,"slug":"intelligent-road-crack-detection-and-analysis","title":"Intelligent road crack detection and analysis based on improved YOLOv8","date":"2025-04-16","arxiv_id":"2504.13208","n_code_links":0,"syntology":null},{"paper":null,"slug":"uncertainty-guided-coarse-to-fine-tumor","title":"Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing","date":"2025-04-16","arxiv_id":"2504.12215","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-based-bathymetry-retrieval","slug":"deep-learning-based-bathymetry-retrieval","title":"Deep Learning-based Bathymetry Retrieval without In-situ Depths using Remote Sensing Imagery and SfM-MVS DSMs with Data Gaps","date":"2025-04-15","arxiv_id":"2504.11416","n_code_links":1,"syntology":null},{"paper":"/paper/due-a-deep-learning-framework-and-library-for","slug":"due-a-deep-learning-framework-and-library-for","title":"DUE: A Deep Learning Framework and Library for Modeling Unknown Equations","date":"2025-04-14","arxiv_id":"2504.10373","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-brain-tumor-segmentation-using-a","title":"Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)","date":"2025-04-14","arxiv_id":"2504.13200","n_code_links":0,"syntology":null},{"paper":null,"slug":"computationally-efficient-signal-detection","title":"Computationally Efficient Signal Detection with Unknown Bandwidths","date":"2025-04-12","arxiv_id":"2504.09342","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-deep-learning-approach-for-emulating","title":"A Novel Deep Learning Approach for Emulating Computationally Expensive Postfire Debris Flows","date":"2025-04-10","arxiv_id":"2504.07736","n_code_links":0,"syntology":null},{"paper":null,"slug":"focal-cortical-dysplasia-type-ii-detection","title":"Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis","date":"2025-04-10","arxiv_id":"2504.07775","n_code_links":0,"syntology":null},{"paper":null,"slug":"impact-of-language-guidance-a-reproducibility","title":"Impact of Language Guidance: A Reproducibility Study","date":"2025-04-10","arxiv_id":"2504.08140","n_code_links":0,"syntology":null},{"paper":null,"slug":"novel-pooling-based-vgg-lite-for-pneumonia","title":"Novel Pooling-based VGG-Lite for Pneumonia and Covid-19 Detection from Imbalanced Chest X-Ray Datasets","date":"2025-04-10","arxiv_id":"2504.07468","n_code_links":0,"syntology":null},{"paper":"/paper/generalized-semantic-contrastive-learning-via","slug":"generalized-semantic-contrastive-learning-via","title":"Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection","date":"2025-04-09","arxiv_id":"2504.07060","n_code_links":1,"syntology":null},{"paper":null,"slug":"cti-unet-cascaded-threshold-integration-for","title":"CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images","date":"2025-04-08","arxiv_id":"2504.05640","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-ensemble-deep-learning-approach-to-detect","title":"An ensemble deep learning approach to detect tumors on Mohs micrographic surgery slides","date":"2025-04-07","arxiv_id":"2504.05219","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-hysteresis-model-of-grain-oriented","title":"Dynamic hysteresis model of grain-oriented ferromagnetic material using neural operators","date":"2025-04-07","arxiv_id":"2504.04863","n_code_links":0,"syntology":null},{"paper":null,"slug":"here-comes-the-explanation-a-shapley","title":"Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation","date":"2025-04-06","arxiv_id":"2504.04645","n_code_links":0,"syntology":null},{"paper":null,"slug":"performance-analysis-of-deep-learning-models","title":"Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan","date":"2025-04-05","arxiv_id":"2504.04066","n_code_links":0,"syntology":null},{"paper":"/paper/dynamic-importance-in-diffusion-u-net-for","slug":"dynamic-importance-in-diffusion-u-net-for","title":"Dynamic Importance in Diffusion U-Net for Enhanced Image Synthesis","date":"2025-04-04","arxiv_id":"2504.03471","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-fpga-accelerated-convolutional","title":"Efficient FPGA-accelerated Convolutional Neural Networks for Cloud Detection on CubeSats","date":"2025-04-04","arxiv_id":"2504.03891","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-encoder-nnu-net-outperforms-transformer","title":"Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining","date":"2025-04-04","arxiv_id":"2504.03474","n_code_links":0,"syntology":null},{"paper":"/paper/haphazard-inputs-as-images-in-online-learning","slug":"haphazard-inputs-as-images-in-online-learning","title":"Haphazard Inputs as Images in Online Learning","date":"2025-04-03","arxiv_id":"2504.02912","n_code_links":1,"syntology":null},{"paper":null,"slug":"semantic-segmentation-of-forest-stands-using","title":"Semantic segmentation of forest stands using deep learning","date":"2025-04-03","arxiv_id":"2504.02471","n_code_links":0,"syntology":null},{"paper":"/paper/cellvta-enhancing-vision-foundation-models","slug":"cellvta-enhancing-vision-foundation-models","title":"CellVTA: Enhancing Vision Foundation Models for Accurate Cell Segmentation and Classification","date":"2025-04-01","arxiv_id":"2504.00784","n_code_links":1,"syntology":null},{"paper":null,"slug":"lightweight-deep-models-for-dermatological","title":"Lightweight Deep Models for Dermatological Disease Detection: A Study on Instance Selection and Channel Optimization","date":"2025-04-01","arxiv_id":"2504.01208","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-study-of-non-complete-ring-positron","title":"Two-stage deep learning framework for the restoration of incomplete-ring PET images","date":"2025-04-01","arxiv_id":"2504.00816","n_code_links":0,"syntology":null},{"paper":null,"slug":"neuralatex-a-machine-learning-library-written","title":"NeuRaLaTeX: A machine learning library written in pure LaTeX","date":"2025-03-31","arxiv_id":"2503.24187","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantum-generative-models-for-image","title":"Quantum Generative Models for Image Generation: Insights from MNIST and MedMNIST","date":"2025-03-30","arxiv_id":"2504.00034","n_code_links":0,"syntology":null},{"paper":"/paper/shiftlic-lightweight-learned-image","slug":"shiftlic-lightweight-learned-image","title":"ShiftLIC: Lightweight Learned Image Compression with Spatial-Channel Shift Operations","date":"2025-03-29","arxiv_id":"2503.23052","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-multi-site-study-on-ai-driven-pathology","title":"A Multi-Site Study on AI-Driven Pathology Detection and Osteoarthritis Grading from Knee X-Ray","date":"2025-03-28","arxiv_id":"2503.22176","n_code_links":0,"syntology":null},{"paper":null,"slug":"autonomous-ai-for-multi-pathology-detection","title":"Autonomous AI for Multi-Pathology Detection in Chest X-Rays: A Multi-Site Study in the Indian Healthcare System","date":"2025-03-28","arxiv_id":"2504.00022","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-building-roof-type-classification-a","title":"Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach","date":"2025-03-28","arxiv_id":"2503.22251","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-dance-to-music-generation-via","title":"Enhancing Dance-to-Music Generation via Negative Conditioning Latent Diffusion Model","date":"2025-03-28","arxiv_id":"2503.22138","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-image-video-and-audio","title":"Comparative Analysis of Image, Video, and Audio Classifiers for Automated News Video Segmentation","date":"2025-03-27","arxiv_id":"2503.21848","n_code_links":0,"syntology":null},{"paper":"/paper/dynamictrl-rethinking-the-basic-structure-and","slug":"dynamictrl-rethinking-the-basic-structure-and","title":"DynamiCtrl: Rethinking the Basic Structure and the Role of Text for High-quality Human Image Animation","date":"2025-03-27","arxiv_id":"2503.21246","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-pavement-crack-classification-with","title":"Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks","date":"2025-03-27","arxiv_id":"2503.21956","n_code_links":0,"syntology":null},{"paper":"/paper/progressive-rendering-distillation-adapting","slug":"progressive-rendering-distillation-adapting","title":"Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data","date":"2025-03-27","arxiv_id":"2503.21694","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-weakly-supervised-deep-learning-model-for","title":"A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI)","date":"2025-03-26","arxiv_id":"2503.20722","n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-driven-mri-spine-pathology-detection-a","title":"AI-Driven MRI Spine Pathology Detection: A Comprehensive Deep Learning Approach for Automated Diagnosis in Diverse Clinical Settings","date":"2025-03-26","arxiv_id":"2503.20316","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-xception-unet-axunet-a-novel","title":"Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation","date":"2025-03-26","arxiv_id":"2503.20446","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-pyramid-network-for-efficient","title":"Dynamic Pyramid Network for Efficient Multimodal Large Language Model","date":"2025-03-26","arxiv_id":"2503.20322","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-robustness-of-cortical-morphometry","title":"Exploring Robustness of Cortical Morphometry in the presence of white matter lesions, using Diffusion Models for Lesion Filling","date":"2025-03-26","arxiv_id":"2503.20571","n_code_links":0,"syntology":null},{"paper":null,"slug":"ita-mdt-image-timestep-adaptive-masked","title":"ITA-MDT: Image-Timestep-Adaptive Masked Diffusion Transformer Framework for Image-Based Virtual Try-On","date":"2025-03-26","arxiv_id":"2503.20418","n_code_links":0,"syntology":null},{"paper":"/paper/learning-from-spatially-inhomogenous-data","slug":"learning-from-spatially-inhomogenous-data","title":"Learning from spatially inhomogenous data: resolution-adaptive convolutions for multiple sclerosis lesion segmentation","date":"2025-03-26","arxiv_id":"2503.21829","n_code_links":1,"syntology":null},{"paper":"/paper/world-model-agents-with-change-based","slug":"world-model-agents-with-change-based","title":"World Model Agents with Change-Based Intrinsic Motivation","date":"2025-03-26","arxiv_id":"2503.21047","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-based-hypoglycemia","title":"Deep Learning-Based Hypoglycemia Classification Across Multiple Prediction Horizons","date":"2025-03-25","arxiv_id":"2504.00009","n_code_links":0,"syntology":null},{"paper":null,"slug":"grn-a-simplified-generative-reinforcement","title":"GRN+: A Simplified Generative Reinforcement Network for Tissue Layer Analysis in 3D Ultrasound Images for Chronic Low-back Pain","date":"2025-03-25","arxiv_id":"2503.19736","n_code_links":0,"syntology":null},{"paper":"/paper/amd-hummingbird-towards-an-efficient-text-to","slug":"amd-hummingbird-towards-an-efficient-text-to","title":"AMD-Hummingbird: Towards an Efficient Text-to-Video Model","date":"2025-03-24","arxiv_id":"2503.18559","n_code_links":1,"syntology":null},{"paper":null,"slug":"pso-unet-particle-swarm-optimized-u-net","title":"PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation","date":"2025-03-24","arxiv_id":"2503.19152","n_code_links":0,"syntology":null},{"paper":"/paper/u-repa-aligning-diffusion-u-nets-to-vits","slug":"u-repa-aligning-diffusion-u-nets-to-vits","title":"U-REPA: Aligning Diffusion U-Nets to ViTs","date":"2025-03-24","arxiv_id":"2503.18414","n_code_links":1,"syntology":null},{"paper":null,"slug":"unsupervised-detection-of-fraudulent","title":"Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning","date":"2025-03-24","arxiv_id":"2503.18841","n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-based-screening-for-depression-and-social","title":"AI-Based Screening for Depression and Social Anxiety Through Eye Tracking: An Exploratory Study","date":"2025-03-22","arxiv_id":"2503.17625","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-diagnosis-of-lung-diseases-using","title":"Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification","date":"2025-03-22","arxiv_id":"2503.18973","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-statistical-theory-of-contrastive-learning","title":"A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics","date":"2025-03-21","arxiv_id":"2503.17538","n_code_links":0,"syntology":null},{"paper":"/paper/decouple-and-track-benchmarking-and-improving","slug":"decouple-and-track-benchmarking-and-improving","title":"Decouple and Track: Benchmarking and Improving Video Diffusion Transformers for Motion Transfer","date":"2025-03-21","arxiv_id":"2503.17350","n_code_links":1,"syntology":null},{"paper":"/paper/end-to-end-sketch-guided-path-planning","slug":"end-to-end-sketch-guided-path-planning","title":"End-to-end Sketch-Guided Path Planning through Imitation Learning for Autonomous Mobile Robots","date":"2025-03-21","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/event-based-crossing-dataset-ebcd","slug":"event-based-crossing-dataset-ebcd","title":"Event-Based Crossing Dataset (EBCD)","date":"2025-03-21","arxiv_id":"2503.17499","n_code_links":1,"syntology":null}],"record_sha256":"5e726d574527e62775fc4099870ca1460520c60a43c497a1649ba8ef60504b6e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}