{"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/squeeze-and-excitation-block/papers/2","list_of":"/method/squeeze-and-excitation-block","method":"Squeeze-and-Excitation Block","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":6,"rows_per_page":100,"rows":[101,200],"of":543,"counts":{"archive_papers_tagged":543,"with_a_code_link":255,"where_syntology_ran_a_sample":74,"not_listed_spam_title":0,"listed":543,"listed_where_code_ran":74,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":67,"every_run_a_failure_of_syntologys_instrument":7,"listed_with_a_run_with_no_instrument_failure":67,"listed_every_run_a_failure_of_syntologys_instrument":7,"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/squeeze-and-excitation-block","prev":"/method/squeeze-and-excitation-block","next":"/method/squeeze-and-excitation-block/papers/3","papers":[{"paper":null,"slug":"credal-wrapper-of-model-averaging-for","title":"Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification","date":"2024-05-23","arxiv_id":"2405.15047","n_code_links":0,"syntology":null},{"paper":null,"slug":"ghost-stereo-ghostnet-based-cost-volume","title":"Ghost-Stereo: GhostNet-based Cost Volume Enhancement and Aggregation for Stereo Matching Networks","date":"2024-05-23","arxiv_id":"2405.14520","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-the-analysis-of-murine-neonatal","slug":"enhancing-the-analysis-of-murine-neonatal","title":"Enhancing the analysis of murine neonatal ultrasonic vocalizations: Development, evaluation, and application of different mathematical models","date":"2024-05-17","arxiv_id":"2405.12957","n_code_links":1,"syntology":null},{"paper":null,"slug":"residual-nerf-learning-residual-nerfs-for","title":"Residual-NeRF: Learning Residual NeRFs for Transparent Object Manipulation","date":"2024-05-10","arxiv_id":"2405.06181","n_code_links":0,"syntology":null},{"paper":null,"slug":"revolutionizing-traffic-sign-recognition","title":"Revolutionizing Traffic Sign Recognition: Unveiling the Potential of Vision Transformers","date":"2024-04-29","arxiv_id":"2404.19066","n_code_links":0,"syntology":null},{"paper":"/paper/guided-absolutegrad-magnitude-of-gradients","slug":"guided-absolutegrad-magnitude-of-gradients","title":"Guided AbsoluteGrad: Magnitude of Gradients Matters to Explanation's Localization and Saliency","date":"2024-04-23","arxiv_id":"2404.15564","n_code_links":1,"syntology":null},{"paper":null,"slug":"depth-estimation-using-weighted-loss-and","title":"Depth Estimation using Weighted-loss and Transfer Learning","date":"2024-04-11","arxiv_id":"2404.07686","n_code_links":0,"syntology":null},{"paper":"/paper/covid-19-detection-from-ct-scans-using","slug":"covid-19-detection-from-ct-scans-using","title":"COVID-19 detection from pulmonary CT scans using a novel EfficientNet with attention mechanism","date":"2024-03-18","arxiv_id":"2403.11505","n_code_links":1,"syntology":null},{"paper":null,"slug":"hsemotion-team-at-the-6th-abaw-competition","title":"HSEmotion Team at the 6th ABAW Competition: Facial Expressions, Valence-Arousal and Emotion Intensity Prediction","date":"2024-03-18","arxiv_id":"2403.11590","n_code_links":0,"syntology":null},{"paper":"/paper/monkeypox-disease-recognition-model-based-on","slug":"monkeypox-disease-recognition-model-based-on","title":"Monkeypox disease recognition model based on improved SE-InceptionV3","date":"2024-03-15","arxiv_id":"2403.10087","n_code_links":1,"syntology":null},{"paper":"/paper/quantization-effects-on-neural-networks","slug":"quantization-effects-on-neural-networks","title":"Quantization Effects on Neural Networks Perception: How would quantization change the perceptual field of vision models?","date":"2024-03-15","arxiv_id":"2403.09939","n_code_links":1,"syntology":null},{"paper":null,"slug":"impact-of-synthetic-images-on-morphing-attack","title":"Impact of Synthetic Images on Morphing Attack Detection Using a Siamese Network","date":"2024-03-14","arxiv_id":"2403.09380","n_code_links":0,"syntology":null},{"paper":"/paper/karina-an-efficient-deep-learning-model-for","slug":"karina-an-efficient-deep-learning-model-for","title":"KARINA: An Efficient Deep Learning Model for Global Weather Forecast","date":"2024-03-13","arxiv_id":"2403.10555","n_code_links":1,"syntology":null},{"paper":"/paper/multiscale-low-frequency-memory-network-for","slug":"multiscale-low-frequency-memory-network-for","title":"Multiscale Low-Frequency Memory Network for Improved Feature Extraction in Convolutional Neural Networks","date":"2024-03-13","arxiv_id":"2403.08157","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-new-machine-learning-dataset-of-bulldog","title":"A New Machine Learning Dataset of Bulldog Nostril Images for Stenosis Degree Classification","date":"2024-03-11","arxiv_id":"2403.07132","n_code_links":0,"syntology":null},{"paper":null,"slug":"cdse-unet-enhancing-covid-19-ct-image","title":"CDSE-UNet: Enhancing COVID-19 CT Image Segmentation with Canny Edge Detection and Dual-Path SENet Feature Fusion","date":"2024-03-03","arxiv_id":"2403.01513","n_code_links":0,"syntology":null},{"paper":"/paper/multi-objective-differentiable-neural","slug":"multi-objective-differentiable-neural","title":"Multi-objective Differentiable Neural Architecture Search","date":"2024-02-28","arxiv_id":"2402.18213","n_code_links":1,"syntology":null},{"paper":null,"slug":"survival-and-grade-of-the-glioma-prediction","title":"Survival and grade of the glioma prediction using transfer learning","date":"2024-02-04","arxiv_id":"2402.03384","n_code_links":0,"syntology":null},{"paper":null,"slug":"hequant-marrying-homomorphic-encryption-and","title":"HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference","date":"2024-01-29","arxiv_id":"2401.15970","n_code_links":0,"syntology":null},{"paper":"/paper/seer-facilitating-structured-reasoning-and","slug":"seer-facilitating-structured-reasoning-and","title":"SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning","date":"2024-01-24","arxiv_id":"2401.13246","n_code_links":1,"syntology":{"ran":13,"of":16,"n_ran_checked":8,"n_instrument":5,"unverified":3,"pointer_only":2,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","official":{"repos":["chen-gx/seer"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/mixnet-towards-effective-and-efficient-uhd","slug":"mixnet-towards-effective-and-efficient-uhd","title":"MixNet: Efficient Global Modeling for Ultra-High-Definition Image Restoration","date":"2024-01-19","arxiv_id":"2401.10666","n_code_links":2,"syntology":null},{"paper":null,"slug":"survival-analysis-of-young-triple-negative","title":"Survival Analysis of Young Triple-Negative Breast Cancer Patients","date":"2024-01-15","arxiv_id":"2401.08712","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-strong-inductive-bias-gzip-for-binary-image","title":"A Strong Inductive Bias: Gzip for binary image classification","date":"2024-01-14","arxiv_id":"2401.07392","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-modules-improve-modern-image-level","title":"Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study","date":"2024-01-13","arxiv_id":"2401.08686","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-uav-based-airport-pavement","title":"Automatic UAV-based Airport Pavement Inspection Using Mixed Real and Virtual Scenarios","date":"2024-01-11","arxiv_id":"2401.06019","n_code_links":0,"syntology":null},{"paper":null,"slug":"senet-visual-detection-of-online-social","title":"SENet: Visual Detection of Online Social Engineering Attack Campaigns","date":"2024-01-10","arxiv_id":"2401.05569","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-deep-convolutional","title":"Comparative Analysis of Deep Convolutional Neural Networks for Detecting Medical Image Deepfakes","date":"2024-01-08","arxiv_id":"2406.08758","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-attacks-on-image-classification-1","title":"Adversarial Attacks on Image Classification Models: Analysis and Defense","date":"2023-12-28","arxiv_id":"2312.16880","n_code_links":0,"syntology":null},{"paper":null,"slug":"roi-aware-multiscale-cross-attention-vision","title":"ROI-Aware Multiscale Cross-Attention Vision Transformer for Pest Image Identification","date":"2023-12-28","arxiv_id":"2312.16914","n_code_links":0,"syntology":null},{"paper":"/paper/accelerating-neural-network-training-a-brief","slug":"accelerating-neural-network-training-a-brief","title":"Accelerating Neural Network Training: A Brief Review","date":"2023-12-15","arxiv_id":"2312.10024","n_code_links":1,"syntology":null},{"paper":null,"slug":"model-evaluation-for-domain-identification-of","title":"Model Evaluation for Domain Identification of Unknown Classes in Open-World Recognition: A Proposal","date":"2023-12-09","arxiv_id":"2312.05454","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparative-analysis-towards-melanoma","title":"A Comparative Analysis Towards Melanoma Classification Using Transfer Learning by Analyzing Dermoscopic Images","date":"2023-12-02","arxiv_id":"2312.01212","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-the-extra-ordinary-validating","slug":"understanding-the-extra-ordinary-validating","title":"Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations","date":"2023-11-28","arxiv_id":"2311.16681","n_code_links":1,"syntology":{"ran":3,"of":6,"n_ran_checked":3,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["maxdreyer/pcx"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"machine-learning-based-jamun-leaf-disease","title":"Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review","date":"2023-11-27","arxiv_id":"2311.15741","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-reusable-ai-enabled-defect-detection-system","title":"A Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN","date":"2023-11-24","arxiv_id":"2311.14824","n_code_links":0,"syntology":null},{"paper":null,"slug":"phytnet-tailored-convolutional-neural","title":"PhytNet -- Tailored Convolutional Neural Networks for Custom Botanical Data","date":"2023-11-20","arxiv_id":"2311.12088","n_code_links":0,"syntology":null},{"paper":"/paper/senetv2-aggregated-dense-layer-for","slug":"senetv2-aggregated-dense-layer-for","title":"SENetV2: Aggregated dense layer for channelwise and global representations","date":"2023-11-17","arxiv_id":"2311.10807","n_code_links":1,"syntology":null},{"paper":null,"slug":"performance-of-machine-learning","title":"Performance of Machine Learning Classification in Mammography Images using BI-RADS","date":"2023-11-14","arxiv_id":"2311.08493","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparative-study-of-knowledge-transfer","title":"Supervised domain adaptation for building extraction from off-nadir aerial images","date":"2023-11-07","arxiv_id":"2311.03867","n_code_links":0,"syntology":null},{"paper":null,"slug":"p-age-pexels-dataset-for-robust-spatio","title":"P-Age: Pexels Dataset for Robust Spatio-Temporal Apparent Age Classification","date":"2023-11-04","arxiv_id":"2311.02432","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-for-plant-identification-and","slug":"deep-learning-for-plant-identification-and","title":"Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches","date":"2023-10-25","arxiv_id":"2310.16273","n_code_links":1,"syntology":null},{"paper":null,"slug":"deepfake-detection-leveraging-the-power-of-2d","title":"Deepfake Detection: Leveraging the Power of 2D and 3D CNN Ensembles","date":"2023-10-25","arxiv_id":"2310.16388","n_code_links":0,"syntology":null},{"paper":null,"slug":"unleashing-modified-deep-learning-models-in","title":"Unleashing Modified Deep Learning Models in Efficient COVID19 Detection","date":"2023-10-21","arxiv_id":"2310.14081","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-car-model-identification-system-for","title":"A Car Model Identification System for Streamlining the Automobile Sales Process","date":"2023-10-19","arxiv_id":"2310.13198","n_code_links":0,"syntology":null},{"paper":"/paper/runner-re-identification-from-single-view","slug":"runner-re-identification-from-single-view","title":"Runner re-identification from single-view running video in the open-world setting","date":"2023-10-18","arxiv_id":"2310.11700","n_code_links":1,"syntology":null},{"paper":"/paper/automatic-segmentation-of-lung-findings-in-ct","slug":"automatic-segmentation-of-lung-findings-in-ct","title":"Automatic segmentation of lung findings in CT and application to Long COVID","date":"2023-10-13","arxiv_id":"2310.09446","n_code_links":1,"syntology":null},{"paper":"/paper/seer-a-knapsack-approach-to-exemplar","slug":"seer-a-knapsack-approach-to-exemplar","title":"SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA","date":"2023-10-10","arxiv_id":"2310.06675","n_code_links":1,"syntology":{"ran":6,"of":16,"n_ran_checked":5,"n_instrument":1,"unverified":10,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 10 unverified","official":{"repos":["jtonglet/seer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":10,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"advancing-diagnostic-precision-leveraging","title":"Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images","date":"2023-10-09","arxiv_id":"2310.06080","n_code_links":0,"syntology":null},{"paper":null,"slug":"incorporating-ensemble-and-transfer-learning","title":"Incorporating Ensemble and Transfer Learning For An End-To-End Auto-Colorized Image Detection Model","date":"2023-09-25","arxiv_id":"2309.14478","n_code_links":0,"syntology":null},{"paper":null,"slug":"improved-breast-cancer-diagnosis-through","title":"Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images","date":"2023-09-15","arxiv_id":"2309.08745","n_code_links":0,"syntology":null},{"paper":null,"slug":"ohq-on-chip-hardware-aware-quantization","title":"On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks","date":"2023-09-05","arxiv_id":"2309.01945","n_code_links":0,"syntology":null},{"paper":null,"slug":"performance-analysis-of-various-efficientnet","title":"Performance Analysis of Various EfficientNet Based U-Net++ Architecture for Automatic Building Extraction from High Resolution Satellite Images","date":"2023-09-05","arxiv_id":"2310.06847","n_code_links":0,"syntology":null},{"paper":null,"slug":"user-lung-cancer-classification-using","title":"User lung cancer classification using efficientnet from ct scan images","date":"2023-09-03","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-detection-of-social-spambots-in","title":"Multimodal Detection of Bots on X (Twitter) using Transformers","date":"2023-08-28","arxiv_id":"2308.14484","n_code_links":0,"syntology":null},{"paper":"/paper/ceimven-an-approach-of-cutting-edge","slug":"ceimven-an-approach-of-cutting-edge","title":"CEIMVEN: An Approach of Cutting Edge Implementation of Modified Versions of EfficientNet (V1-V2) Architecture for Breast Cancer Detection and Classification from Ultrasound Images","date":"2023-08-25","arxiv_id":"2308.13356","n_code_links":2,"syntology":null},{"paper":null,"slug":"falcon-accelerating-homomorphically-encrypted","title":"Falcon: Accelerating Homomorphically Encrypted Convolutions for Efficient Private Mobile Network Inference","date":"2023-08-25","arxiv_id":"2308.13189","n_code_links":0,"syntology":null},{"paper":"/paper/tpugraphs-a-performance-prediction-dataset-on-1","slug":"tpugraphs-a-performance-prediction-dataset-on-1","title":"TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs","date":"2023-08-25","arxiv_id":"2308.13490","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["google-research-datasets/tpu_graphs"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/integrated-image-and-location-analysis-for","slug":"integrated-image-and-location-analysis-for","title":"Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach","date":"2023-08-23","arxiv_id":"2308.11877","n_code_links":1,"syntology":null},{"paper":"/paper/mixnet-toward-accurate-detection-of","slug":"mixnet-toward-accurate-detection-of","title":"MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild","date":"2023-08-23","arxiv_id":"2308.12817","n_code_links":1,"syntology":null},{"paper":"/paper/from-hope-to-safety-unlearning-biases-of-deep","slug":"from-hope-to-safety-unlearning-biases-of-deep","title":"From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space","date":"2023-08-18","arxiv_id":"2308.09437","n_code_links":1,"syntology":null},{"paper":null,"slug":"seer-super-optimization-explorer-for-hls","title":"SEER: Super-Optimization Explorer for HLS using E-graph Rewriting with MLIR","date":"2023-08-15","arxiv_id":"2308.07654","n_code_links":0,"syntology":null},{"paper":null,"slug":"gaussian-image-anomaly-detection-with-greedy","title":"Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection","date":"2023-08-09","arxiv_id":"2308.04944","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-lightweight-and-accurate-face-detection","title":"A Lightweight and Accurate Face Detection Algorithm Based on Retinaface","date":"2023-08-08","arxiv_id":"2308.04340","n_code_links":0,"syntology":null},{"paper":"/paper/deep-semantic-model-fusion-for-ancient","slug":"deep-semantic-model-fusion-for-ancient","title":"Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection","date":"2023-08-04","arxiv_id":"2308.02225","n_code_links":1,"syntology":null},{"paper":null,"slug":"food-classification-using-joint","title":"Food Classification using Joint Representation of Visual and Textual Data","date":"2023-08-03","arxiv_id":"2308.02562","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparison-between-transformers-and","title":"Comparison between transformers and convolutional models for fine-grained classification of insects","date":"2023-07-20","arxiv_id":"2307.11112","n_code_links":0,"syntology":null},{"paper":"/paper/repvit-revisiting-mobile-cnn-from-vit","slug":"repvit-revisiting-mobile-cnn-from-vit","title":"RepViT: Revisiting Mobile CNN From ViT Perspective","date":"2023-07-18","arxiv_id":"2307.09283","n_code_links":8,"syntology":{"ran":6,"of":12,"n_ran_checked":4,"n_instrument":2,"unverified":6,"pointer_only":5,"phrase":"6 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; 2 where Syntology's instrument failed) · 6 unverified","official":null}},{"paper":null,"slug":"sephrnet-generating-high-resolution-crop-maps","title":"SepHRNet: Generating High-Resolution Crop Maps from Remote Sensing imagery using HRNet with Separable Convolution","date":"2023-07-11","arxiv_id":"2307.05700","n_code_links":0,"syntology":null},{"paper":null,"slug":"art-authentication-with-vision-transformers","title":"Art Authentication with Vision Transformers","date":"2023-07-06","arxiv_id":"2307.03039","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-and-fully-automatic-retinal-choroid","slug":"efficient-and-fully-automatic-retinal-choroid","title":"An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography","date":"2023-07-03","arxiv_id":"2307.00904","n_code_links":1,"syntology":null},{"paper":null,"slug":"streamlined-lensed-quasar-identification-in","title":"Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks","date":"2023-07-03","arxiv_id":"2307.01090","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multi-task-learning-framework-for-carotid","title":"A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification","date":"2023-07-02","arxiv_id":"2307.00583","n_code_links":0,"syntology":null},{"paper":null,"slug":"ncis-deep-color-gradient-maps-regression-and","title":"NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images","date":"2023-06-27","arxiv_id":"2306.15784","n_code_links":0,"syntology":null},{"paper":"/paper/sar-atr-under-limited-training-data-via","slug":"sar-atr-under-limited-training-data-via","title":"SAR ATR under Limited Training Data Via MobileNetV3","date":"2023-06-27","arxiv_id":"2306.15287","n_code_links":1,"syntology":null},{"paper":null,"slug":"edge-devices-inference-performance-comparison","title":"Edge Devices Inference Performance Comparison","date":"2023-06-21","arxiv_id":"2306.12093","n_code_links":0,"syntology":null},{"paper":"/paper/renderers-are-good-zero-shot-representation","slug":"renderers-are-good-zero-shot-representation","title":"Renderers are Good Zero-Shot Representation Learners: Exploring Diffusion Latents for Metric Learning","date":"2023-06-19","arxiv_id":"2306.10721","n_code_links":1,"syntology":null},{"paper":null,"slug":"systematic-architectural-design-of-scale","title":"Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis","date":"2023-06-16","arxiv_id":"2306.10128","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-neural-network-compression-via","title":"End-to-End Neural Network Compression via $\\frac{\\ell_1}{\\ell_2}$ Regularized Latency Surrogates","date":"2023-06-09","arxiv_id":"2306.05785","n_code_links":0,"syntology":null},{"paper":"/paper/revising-deep-learning-methods-in-parking-lot","slug":"revising-deep-learning-methods-in-parking-lot","title":"Revising deep learning methods in parking lot occupancy detection","date":"2023-06-07","arxiv_id":"2306.04288","n_code_links":1,"syntology":null},{"paper":"/paper/hiding-in-plain-sight-disguising-data","slug":"hiding-in-plain-sight-disguising-data","title":"Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning","date":"2023-06-05","arxiv_id":"2306.03013","n_code_links":2,"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":"spot-keywords-from-very-noisy-and-mixed","title":"Spot keywords from very noisy and mixed speech","date":"2023-05-28","arxiv_id":"2305.17706","n_code_links":0,"syntology":null},{"paper":"/paper/diffusionnag-task-guided-neural-architecture","slug":"diffusionnag-task-guided-neural-architecture","title":"DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models","date":"2023-05-26","arxiv_id":"2305.16943","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["cownowan/diffusionnag"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"thailand-asset-value-estimation-using-aerial","title":"Thailand Asset Value Estimation Using Aerial or Satellite Imagery","date":"2023-05-26","arxiv_id":"2307.08650","n_code_links":0,"syntology":null},{"paper":null,"slug":"mask-attack-detection-using-vascular-weighted","title":"Mask Attack Detection Using Vascular-weighted Motion-robust rPPG Signals","date":"2023-05-25","arxiv_id":"2305.15940","n_code_links":0,"syntology":null},{"paper":null,"slug":"augmented-random-search-for-multi-objective","title":"Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML","date":"2023-05-23","arxiv_id":"2305.14109","n_code_links":0,"syntology":null},{"paper":null,"slug":"increasing-melanoma-diagnostic-confidence","title":"Increasing Melanoma Diagnostic Confidence: Forcing the Convolutional Network to Learn from the Lesion","date":"2023-05-16","arxiv_id":"2305.09542","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-classification-of-stroke-blood-clot","title":"Advancing Ischemic Stroke Diagnosis: A Novel Two-Stage Approach for Blood Clot Origin Identification","date":"2023-04-26","arxiv_id":"2304.13775","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":"/paper/layernas-neural-architecture-search-in","slug":"layernas-neural-architecture-search-in","title":"LayerNAS: Neural Architecture Search in Polynomial Complexity","date":"2023-04-23","arxiv_id":"2304.11517","n_code_links":0,"syntology":null},{"paper":"/paper/watt-effnet-a-lightweight-and-accurate-model","slug":"watt-effnet-a-lightweight-and-accurate-model","title":"WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images","date":"2023-04-21","arxiv_id":"2304.10811","n_code_links":1,"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":"efficientnet-algorithm-for-classification-of","title":"EfficientNet Algorithm for Classification of Different Types of Cancer","date":"2023-04-18","arxiv_id":"2304.08715","n_code_links":0,"syntology":null},{"paper":null,"slug":"ensemble-cnns-for-breast-tumor-classification","title":"Ensemble CNNs for Breast Tumor Classification","date":"2023-04-11","arxiv_id":"2304.13727","n_code_links":0,"syntology":null},{"paper":"/paper/fastvit-a-fast-hybrid-vision-transformer","slug":"fastvit-a-fast-hybrid-vision-transformer","title":"FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization","date":"2023-03-24","arxiv_id":"2303.14189","n_code_links":6,"syntology":{"ran":1,"of":5,"n_ran_checked":1,"n_instrument":0,"unverified":4,"pointer_only":5,"phrase":"1 ran (of which 1 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) · 4 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["apple/ml-fastvit","rwightman/pytorch-image-models"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/magiceye-an-intelligent-wearable-towards","slug":"magiceye-an-intelligent-wearable-towards","title":"MagicEye: An Intelligent Wearable Towards Independent Living of Visually Impaired","date":"2023-03-24","arxiv_id":"2303.13863","n_code_links":1,"syntology":null},{"paper":"/paper/reveal-to-revise-an-explainable-ai-life-cycle","slug":"reveal-to-revise-an-explainable-ai-life-cycle","title":"Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models","date":"2023-03-22","arxiv_id":"2303.12641","n_code_links":1,"syntology":null},{"paper":null,"slug":"bias-mitigation-techniques-in-image","title":"Bias mitigation techniques in image classification: fair machine learning in human heritage collections","date":"2023-03-20","arxiv_id":"2303.11449","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-learning-based-wireless-image","title":"End-to-End Learning-Based Wireless Image Recognition Using the PyramidNet in Edge Intelligence","date":"2023-03-16","arxiv_id":"2303.09188","n_code_links":0,"syntology":null},{"paper":"/paper/reinforce-data-multiply-impact-improved-model","slug":"reinforce-data-multiply-impact-improved-model","title":"Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement","date":"2023-03-15","arxiv_id":"2303.08983","n_code_links":1,"syntology":null}],"record_sha256":"eae2929a3d01a4767f7651c909e792a63776d611d244fdc39b89a9a2edcf3388","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}